text to speech fpt

Link donate : : https://www.facebook.com/groups/808719699605259Fan Page: https://www.facebook.com/Lirs-Tech-Tips-111449010 Addeddate 2019-04-06 14:28:46 Identifier FPt_alathar Identifier-ark ark:/13960/t1rg39s1c Ocr language not currently OCRable Page-progression rl Ppi 400 Scanner 4 Answers. You might wanna look at react-native-android-voice, a React Native module that supports speech-to-text for Android. As @delkant mentioned, there's now also react-native-voice that supports both Android and iOS. Alternatively, you can always write your custom native module using Android's SpeechRecognizer API. Ionic Text to Speech: How to Convert Text to Speech in Ionic Application using Ionic Native and Cordova Plugins (Có thử tiếng Việt ok) Posted on 11/01/2021 by soiqualang_chentreu Posted in Công nghệ, Sói's Tutorials, Tài liệu Tagged Ionic, Mobile, Text to Speech, tts Leave a Comment. Read More. An extension for FPT University by RubyBar Date: 14/07/2021 ----- This software supports students to calculate the average score of each student's semester. Read Aloud: A Text to Speech Voice Reader. 2,511. Ad. Added. Text reader (TTS) that simplifies vocabulary, translates text, reads inaccessible text (OCR), and captures and cites sources Text to speech Khởi tạo các tham số. Keys xin phép được ẩn đi. Mình chẳng tiếc nhưng sợ đông người dùng các bạn làm chậm của nhau. Backup file mp3 trước đó. Gọi API và tải các file nhạc. Vì fpt giới hạn số lượng ký tự. Nên ta cần cắt ra thành nhiều đoạn. Cắt thế nào để không Gộp files. Sau bước cagukine1972. © 2020 - 2023 TTSFree. All rights reserved. Texto para fala online grátis. Converter texto em voz com vozes naturais. Baixar mp3 grátis. Conversor de texto em fala online. Converta seu texto em 177 vozes de som natural. Download de mp3 grátis. Português Portugal - Portuguese Text to Speech voices There are 11 Portuguese Portugal voices, including male and female. To listen to the voice demo, click the "Play" icon. Duarte, Male voiceID pt-PT-DuarteNeural Fernanda, Female voiceID pt-PT-FernandaNeural Raquel, Female voiceID pt-PT-RaquelNeural Wavenet-A, Female Premium voiceID pt-PT-Wavenet-A Wavenet-B, Male Premium voiceID pt-PT-Wavenet-B Wavenet-C, Male Premium voiceID pt-PT-Wavenet-C Wavenet-D, Female Premium voiceID pt-PT-Wavenet-D Standard-A, Female voiceID pt-PT-Standard-A Standard-B, Male voiceID pt-PT-Standard-B Standard-C, Male voiceID pt-PT-Standard-C Standard-D, Female voiceID pt-PT-Standard-D Text-to-speech Portugal additional regional language versions To see more other regional Portugal text-to-speech, see the pages below Maneira fácil de converter texto em fala TTS gratuitamente Apenas 3 passos abaixo, rápido e simples para converter texto em fala TTS gratuitamente. ENTRADA DE TEXTO PARA FALA Personalize sua fala com controles de tom e velocidade de voz. Faça seu discurso mais rápido ou mais lento, assuma o controle do volume da voz. SELECIONE LANGUAGE & VOICE Selecione o idioma e o leitor de que você precisa para converter o texto em mp3. Ajuste o volume, a velocidade da voz, do seu jeito. CONVERTER E BAIXAR MP3 O processo de conversão de texto em voz é muito rápido, o resultado será o formato mp3. Você pode começar a baixar para o seu trabalho. Leitor de texto online voz natural é um site de conversão de texto em voz online GRATUITO TTS Free baseado na tecnologia AI. Temos mais de 200 vozes AI padrão e vozes humanas naturais, em mais de 50 idiomas em todo o mundo. Você pode usar nossa voz para o seu trabalho, bem como criar seus próprios vídeos para colocar no Facebook, YouTube, Vimeo, Instagram ou sites pessoais. O TTS gratuito usa inteligência artificial IA e aprendizado de máquina ML, tecnologias líderes do Google e da Microsoft, permitindo-nos ultrapassar os limites e criar um Text-to-Speech que é muito humano, como sons personalizáveis, velocidade de voz, tom, volume , pausar, adicionar ênfase, formato de áudio e configurações de perfil de áudio. O que é TTS? TTS é a abreviatura de Text-to-Speech, uma tecnologia de conversão de texto em fala. Possui diversos aplicativos, gratuitos e pagos. Ele pode ser usado para criar narrações para vídeos, converter documentos de texto em vozes ou ajudar pessoas com problemas de visão a "lerem" o texto. Qual é o melhor texto para fala livre software, aplicativos? Texto grátis para aplicativos de fala para converter qualquer texto em áudio. O melhor software de texto para fala livre tem muitos casos de uso em sua vida computacional. O melhor programa ou software de conversão de texto em voz gratuito pode converter seu texto em voz / fala em apenas alguns segundos. Sugerimos algumas listagens do melhor texto para voz livre que fornece som natural para o seu projeto. 1 2 Fromtexttospeech 3 Natural Reader 4 Google Text-to-Speech 5 Microsoft Azure Cognitive 6 Notevibes Melhor texto para fala ai Usamos a melhor IA do Google Cloud, Microsoft, Amazon Polly, Watson IBM Cloud e várias outras fontes é um serviço de conversão de texto em voz grátis? Sim, Texto Livre para Fala! Fornece o serviço TTS gratuito da mais alta qualidade na Internet. Converter texto em fala, arquivo MP3. Você pode ouvir ou fazer o download. Suporta inglês, francês, alemão, japonês, espanhol, vietnamita ... vários idiomas. Além do plano gratuito, temos planos pagos com recursos avançados, limites aumentados e melhor qualidade de voz. Como funcionam os programas de texto para fala? A maioria das ferramentas de texto para fala funcionam de maneira semelhante. Você deve digitar o texto que deseja converter para voz ou fazer upload de um arquivo de texto. Em seguida, você deve selecionar as vozes disponíveis e visualizar o áudio. Depois de encontrar a voz mais adequada, você pode baixar o arquivo mp3. Suporte à linguagem SSML Speech Synthesis Markup Language? Suporte completo a SSML. Você pode enviar Speech Synthesis Markup Language SSML em sua solicitação Text-to-Speech para permitir mais personalização em sua resposta de áudio, fornecendo detalhes sobre as pausas e formatação de áudio para acrônimos, datas, horas, abreviações, endereços ou texto que deve ser censurado. Consulte o tutorial SSML do Speech-to-Text para obter mais informações e exemplos de código. Text to speech created by luantm FPT đã ra v5 với các giọng hay hơn các bạn có thể tham khảo từ Nếu các bạn quan tâm có thể liên hệ với mình để mua v5 Uses Cmd [ ] if command == 'help' printopen' 'r'.read elif command == 'backup' backupshort_direct elif command == 'remove_files' backupshort_direct elif command == 'download' downloadshort_direct, voice, speed, prosody elif command == 'merge_files' merge_filesshort_direct else run_allshort_direct, voice, speed, prosody Xác định các giọng đọc, voice có các giá trị là leminh giọng nam miền bắc, male giọng nam miền bắc, female giọng nữ miền bắc, hatieumai giọng nữ miền nam, ngoclam giọng nữ Huế voice = "female" Xác định các giọng đọc, voice có các giá trị là leminh giọng nam miền bắc nghe ấm , male giọng nam miền bắc hơi già có tiếng thở, female giọng nữ miền bắc trẻ, giọng trong đọc hơi chậm so với các giọng khác, hatieumai giọng nữ miền nam nghe đk, ngoclam giọng nữ Huế đọc hơi bị ngắt nên cho chậm lại speed= "0" ngữ điệu 1 on. 0 off prosody= "0" please add your api key in To read the full-text of this research, you can request a copy directly from the authors.... modules [26] are chosen because it is free and supports PHP. Besides, contacting for support in Vietnam where the population is approaching 100 million is easier and faster than through other commercialized tools supported by Google or Microsoft since their headquarters are in oversea countries. ...... Besides, contacting for support in Vietnam where the population is approaching 100 million is easier and faster than through other commercialized tools supported by Google or Microsoft since their headquarters are in oversea countries. In addition, the provided tool [26] has advantages in supporting local language, Vietnamese, over other similar products offered by Google or Microsoft. For TTS module testing, random texts from CNN and VnExpress news pages were used. ...In recent years, voicebot has become a popular communication tool between humans and machines. In this paper, we will introduce our voicebot integrating text-to-speech TTS and speech-to-text STT modules provided by This voicebot can be considered as a critical improvement of a typical chatbot because it can respond to human’s queries by both text and speech. FPT Open Speech, LibriSpeech datasets, and music files were used to test the accuracy and performance of the STT module. For the TTS module, it was tested by using text on news pages in both Vietnamese and English. To test the voicebot, Homestay Service topic questions and off-topic messages were input to the system. The TTS module achieved 100% accuracy in the Vietnamese text test and accuracy in the English text test. In the STT module test, the accuracy for FPT open speech dataset Vietnamese is and for LibriSpeech Dataset English is 0% while the accuracy in music files test is 0% for both. The voicebot achieved 100% accuracy in its test. Since the STT and TTS modules were developed to support only Vietnamese for dominating the Vietnam market, it is reasonable that the test with LibriSpeech Dataset resulted in 0% accuracy.... Sultana et al. [6] proposed an approach for Speech-to-Text conversion using Speech Application Programming Interface SAPI [186] for the Bangla language. The authors managed SAPI to combine pronunciation from the spoken continuous Bangla speech with a precompiled grammar file, and then SAPI returned Bangla words in English character if matches occur. ...The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing BNLP trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. Furthermore, we discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.... Paul et al. [3] presented a Bangla speech recognition system utilizing pre-emphasis filtering, speech coding, LPC, and ANN. Sultana et al. [4] developed a technique for converting Bangla speech to text using SAPI [5]. Hasnat et al. [6] developed a strategy for constructing an isolated and continuous Bangla voice recognition system applying HMM toolkit. ...Speech recognition is a technique that converts human speech signals into text or words or in any form that can be easily understood by computers or other machines. There have been a few studies on Bangla digit recognition systems, the majority of which used small datasets with few variations in genders, ages, dialects, and other variables. Audio recordings of Bangladeshi people of various genders, ages, and dialects were used to create a large speech dataset of spoken '০-৯' Bangla digits in this study. Here, 400 noisy and noise-free samples per digit have been recorded for creating the dataset. Mel Frequency Cepstrum Coefficients MFCCs have been utilized for extracting meaningful features from the raw speech data. Then, to detect Bangla numeral digits, Convolutional Neural Networks CNNs were utilized. The suggested technique recognizes '০-৯' Bangla spoken digits with accuracy throughout the whole dataset. The efficiency of the model was also assessed using 10-fold cross-validation, which yielded a accuracy.... Paul et al. [3] presented a Bangla speech recognition system utilizing pre-emphasis filtering, speech coding, LPC, and ANN. Sultana et al. [4] developed a technique for converting Bangla speech to text using SAPI [5]. Hasnat et al. [6] developed a strategy for constructing an isolated and continuous Bangla voice recognition system applying HMM toolkit. ...Speech recognition is a technique that converts human speech signals into text or words or in any form that can be easily understood by computers or other machines. There have been a few studies on Bangla digit recognition systems, the majority of which used small datasets with few variations in genders, ages, dialects, and other variables. Audio recordings of Bangladeshi people of various genders, ages, and dialects were used to create a large speech dataset of spoken '0-9' Bangla digits in this study. Here, 400 noisy and noise-free samples per digit have been recorded for creating the dataset. Mel Frequency Cepstrum Coefficients MFCCs have been utilized for extracting meaningful features from the raw speech data. Then, to detect Bangla numeral digits, Convolutional Neural Networks CNNs were utilized. The suggested technique recognizes '0-9' Bangla spoken digits with accuracy throughout the whole dataset. The efficiency of the model was also assessed using 10-fold crossvalidation, which yielded a accuracy.... Sultana et al. [6] proposed an approach for Speech-to-Text conversion using Speech Application Programming Interface SAPI [165] for the Bangla language. The authors managed SAPI to combine pronunciation from the spoken continuous Bangla speech with a precompiled grammar file, and then SAPI returned Bangla words in English character if matches occur. ...The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing BNLP trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50\% of the papers were published after 2015. We discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.... In the past few years, the development of chatbots [1], [2] and voicebots [3]- [6] are replacing human in performing daily tasks such as registration, gathering information, surveying customers' feedbacks, etc… Therefore, string manipulation and text analysis [7] are becoming more and more important, especially in natural language processing applications, part of which is text-to-speech TTS. For example, due to huge amount of users, the telecommunications corporations should replace operators with auto-bots to perform simple tasks, such as giving user a simple information, phone number, name, or email. ...In recent years, the growth of corporates' businesses worldwide has driven the setups of call centers for supporting global customers 24/7 across the globe. However, many of the call centers are operated by human manually. Thus, there is need for developing automatic call center which is powered by artificial intelligence AI, hence reducing operational costs through automation of calls, answering calls, conducting surveys, and receiving customer feedbacks. In this context, recently developed engines such as serves well local customers since it supports well the national language. However, it's text-to-speech TTS, an essential part of the complete engine, is currently having difficulties in reading customer emails. Therefore, this work presents an email to readable Vietnamese text conversion algorithm for use in TTS application. The average processing time tested on 60 emails is milliseconds ms. By manually validating the dataset, it is found that the algorithm achieves accuracy of up to Nowadays, the advances of technologies in artificial intelligence and machine learning have enabled wide development of automated tools for answering customers' queries, collecting surveys, addressing complaints without human involvements. These tools are usually chatbots [1][2][3][4][5][6], or more advanced, voicebots [3,[7][8][9]. For voicebots, it is essential to have engines called text-to-speech TTS for performing conversion of answering text to speech and playback to customer during a call. ... Tran Duc ChungThis paper presents the first Tacotron-2-based text-to-speech TTS application development for Vietnamese that utilizes the publicly available FPT Open Speech Dataset FOSD containing approximately 30 hours of labeled audio files together with their transcripts. A new cleaner was developed for supporting Vietnamese language rather than English. After 225,000 training steps, the generated speeches have mean opinion score MOS well above the average value of and center around for both clearness and naturalness in a crowd-source speech recognition ASR converts human speech into text or words that can be understood and classified easily. Only digits from 0-9 were used in the few studies on Bangla number recognition systems, which completely ignored duo-syllabic and tri-syllabic numbers. Audio samples of 0-99 Bangla spoken numbers from Bangladeshi citizens of various genders, ages, and languages were used to construct a speech dataset of spoken numbers in this work. Time shift, speed tuning, background noise mixing, and volume tuning are among the audio augmentation techniques used on the raw speech data. Then, to extract meaningful features from the data, Mel Frequency Cepstrum Coefficients MFCCs are used. This research developed a Bangla number recognition system based on Convolutional Neural Networks CNNs. Our proposed dataset includes the diversity of speakers in terms of age, gender, dialects and other criteria. The proposed method recognizes 0-99 Bangla spoken numbers with accuracy across the entire dataset. The model’s efficacy was also evaluated using a 10-fold cross-validation procedure, with accuracy for recognizing 0-99 Bangla spoken numbers across the entire dataset. This proposed method is also compared to some existing works in the field of recognizing spoken digits classes, demonstrating its speech recognitionBangla spoken 0-99 numbers classificationCNNMFCCCross-validationTravis SmithVasilios PappademetriouAdvancements in the affordability and availability of Internet of Things IoT devices have led to incredible innovations in the field of speech-generating devices. The introduction of devices such as tablets, cellphones, and mobile computers has allowed individuals with speech disorders to have a medium in which to easily communicate without purchasing expensive and specialized medical equipment. Even though these devices increased access to this technology, it is still out of reach for many. Whether it is the cost of the device, cost of the speech generating software, or access to a reliable internet connection, this technology is inaccessible to some of the people who need it most. The focus of this project is bringing this voice technology to the oppressed and disadvantaged by creating a fully open-source device assembled with off the shelf parts that is a fraction of the cost of similar alternatives. The goal was to accomplish this with minimal compromises to quality and usability. This was achieved with a Raspberry Pi computer, touch screen, battery pack, and a plastic casing. It overall met the quality and usability expectations of an Alternative Communication AAC device for around $150 USD and shows that traditionally expensive AAC equipment can be made more accessible to people, without compromising usability. It hopefully will motivate others to research areas where off the shelf parts and open-source software can be used to increase the accessibility of otherwise expensive and specialized technologies to benefit the lives of others. Tran Duc ChungIn the past five years, end-to-end E2E text-to-speech TTS application has become of interest for many researches related to speech generation from text. This has been the result of the fast-pace development of artificial intelligence and machine learning, not only in image processing, but also in audio and signal processing. Often, the training and testing datasets of TTS applications comprise of thousands of data in forms of text lines and the corresponding audio files which amount up to tens of recording hours. Thus, it is extremely time-consuming for preparing training and testing dataset using manual approach. This work presents an approach for automatic data preparation that is used in Tacotron, Tacotron-2-based Mozilla TTS engine. The well-labeled dataset namely FPT Open Vietnamese Speech Dataset having over 25,000 text lines and recorded audio files is demonstrated in this work. On average, the algorithm takes approximately 45-70 µs to process one text line. Tran Duc ChungIn recent years, the growth of corporates’ businesses worldwide has driven the setups of call centers for supporting global customers 24/7 across the globe. However, many of the call centers are operated by human manually. Thus, there is need for developing automatic call center which is powered by artificial intelligence AI, hence reducing operational costs through automation of calls, answering calls, conducting surveys, and receiving customer feedbacks. In this context, recently developed engines such as serves well local customers since it supports well the national language. However, it’s text-to-speech TTS, an essential part of the complete engine, is currently having difficulties in reading customer emails. Therefore, this work presents an email to readable Vietnamese text conversion algorithm for use in TTS application. The average processing time tested on 60 emails is milliseconds ms. By manually validating the dataset, it is found that the algorithm achieves accuracy of up to PingKainan PengAndrew GibianskyJohn MillerWe present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech TTS system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU text-to-speech synthesis TTS systems are often perceived as lacking expressiveness, limiting the ability to fully convey information. This paper describes initial investigations into improving expressiveness for statistical speech synthesis systems. Rather than using hand-crafted definitions of expressive classes, an unsupervised clustering approach is described which is scalable to large quantities of training data. To incorporate this "expression cluster" information into an HMM-TTS system two approaches are described cluster questions in the decision tree construction; and average expression speech synthesis AESS using cluster-based linear transform adaptation. The performance of the approaches was evaluated on audiobook data in which the reader exhibits a wide range of expressiveness. A subjective listening test showed that synthesising with AESS results in speech that better reflects the expressiveness of human speech than a baseline expression- independent paper presents an analysis of effects of soft-masking function on spectrogram-based instrument - vocal separation for audio signals. The function taken into consideration is of 1st-order with two masking magnitude parameters one for background and one foreground separation. It is found that as the masking magnitude increases, the signal estimations are improved. The background signal’s spectrogram becomes closer to that of the original signal while the foreground signal’s spectrogram represents better the vocal wiggle lines compared to the original signal spectrogram. With the same increase in the masking magnitude up to ten-fold, the effect on background signal spectrogram is more significant compared to that of foreground signal. This is evident through the significant \\approx \three times reduction of background signal’s root-mean-square RMS values and the less significant reduction approximately one-third of foreground signal’s RMS chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning DRL is promising for addressing this challenge, but its successful application remains an open question. This article describes a novel ensemble-based approach applied to value-based DRL chatbots, which use finite action sets as a form of meaning representation. In our approach, while dialogue actions are derived from sentence clustering, the training datasets in our ensemble are derived from dialogue clustering. The latter aim to induce specialised agents that learn to interact in a particular style. In order to facilitate neural chatbot training using our proposed approach, we assume dialogue data in raw text only – without any manually-labelled data. Experimental results using chitchat data reveal that 1 near human-like dialogue policies can be induced, 2 generalisation to unseen data is a difficult problem, and 3 training an ensemble of chatbot agents is essential for improved performance over using a single agent. In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency – which revealed that our proposed dialogue rewards strongly correlate with human ShenRuoming PangRon J. WeissYonghui WuThis paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms from those spectrograms. Our model achieves a mean opinion score MOS of comparable to a MOS of for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the input to WaveNet instead of linguistic, duration, and F0 features. We further demonstrate that using a compact acoustic intermediate representation enables significant simplification of the WaveNet chatbots also known as chat-agents have attracted much attention from both researching and industrial fields. Generally, the semantic relevance between users' queries and the corresponding responses is considered as the essential element for conversation modeling in both generation and ranking based chat systems. By contrast, it is a non-trivial task to adopt the users' information, such as preference, social role, etc., into conversational models reasonably, while users' profiles play a significant role in the procedure of conversations by providing the implicit contexts. This paper aims to address the personalized response ranking task by incorporating user profiles into the conversation model. In our approach, users' personalized representations are latently learned from the contents posted by them via a two-branch neural network. After that, a deep neural network architecture is further presented to learn the fusion representation of posts, responses and personal information. In this way, the proposed model could understand conversations from the users' perspective, hence the more appropriate responses are selected for a specified person. The experimental results on two datasets from Social Network Services SNS demonstrate that our approach is hopeful to represent users' personal information implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each is known that the performance of a developed text-to-speech TTS synthesis system is assessed by subjective tests. These assessments are usually based on the intelligibility and naturalness of the synthesized speech. In this study, an investigation on relating these subjective test results, thus the naturalness of the synthesized speech, to which acoustic features is accomplished. Consequently the features which will increase the performance while synthesizing the speech are determined. Our work is focused especially on the pitch frequency and energy paper describes the work based on concatenative text-tospeech synthesis system. It discusses a few perceptual and spectrogram experiments conducted on Marathi Voices Spoken in Maharashtra, India. Marathi speech synthesizer is developed using different choice of units words, phonemes as a database. We have synthesized the Marathi text and conducted the perceptual tests, as a result, 1 74% of speech synthesized by the proposed method was preferred to that by the conventional method, 2 the mean opinion score MOS was in a five-point MOS test, and 87% of the synthesized speech had the same naturalness as natural speech 40 samples taken from various slot of databases 3 Histogram for various speech databases shows the effectiveness of the proposed method. 4 Spectrogram analysis of various words concatenated with phonemes, syllables as a knowledge extraction of any Chatbot from conversationS ArsovskiH OsipyanM I OladeleA D Cheok

text to speech fpt