Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis

Neural Text-to-speech (TTS) synthesis is a powerful technology that can generate speech using neural networks. One of the most remarkable features of TTS synthesis is its capability to produce speech in the voice of different speakers. This paper introduces voice cloning and speech synthesis https://pypi.org/project/voice-cloning/ an open-source python package for helping speech disorders to communicate more effectively as well as for professionals seeking to integrate voice cloning or speech synthesis capabilities into their projects. This package aims to generate synthetic speech that sounds like the natural voice of an individual, but it does not replace the natural human voice. The architecture of the system comprises a speaker verification system, a synthesizer, a vocoder, and noise reduction. Speaker verification system trained on a varied set of speakers to achieve optimal generalization performance without relying on transcriptions. Synthesizer is trained using both audio and transcriptions that generate Mel spectrogram from a text and vocoder which converts the generated Mel Spectrogram into corresponding audio signal. Then the audio signal is processed by a noise reduction algorithm to eliminate unwanted noise and enhance speech clarity. The performance of synthesized speech from seen and unseen speakers are then evaluated using subjective and objective evaluation such as Mean Opinion Score (MOS), Gross Pitch Error (GPE), and Spectral distortion (SD). The model can create speech in distinct voices by including speaker characteristics that are chosen randomly

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

주파수 및 시간적 상관관계에 기반한 음향학적 에코 억제 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 김남수.In the past decades, a number of approaches have been dedicated to acoustic echo cancellation and suppression which reduce the negative effects of acoustic echo, namely the acoustic coupling between the loudspeaker and microphone in a room. In particular, the increasing use of full-duplex telecommunication systems has led to the requirement of faster and more reliable acoustic echo cancellation algorithms. The solutions have been based on adaptive filters, but the length of these filters has to be long enough to consider most of the echo signal and linear filtering in these algorithms may be limited to remove the echo signal in various environments. In this thesis, a novel stereophonic acoustic echo suppression (SAES) technique based on spectral and temporal correlations is proposed in the short-time Fourier transform (STFT) domain. Unlike traditional stereophonic acoustic echo cancellation, the proposed algorithm estimates the echo spectra in the STFT domain and uses a Wiener filter to suppress echo without performing any explicit double-talk detection. The proposed approach takes account of interdependencies among components in adjacent time frames and frequency bins, which enables more accurate estimation of the echo signals. Due to the limitations of power amplifiers or loudspeakers, the echo signals captured in the microphones are not in a linear relationship with the far-end signals even when the echo path is perfectly linear. The nonlinear components of the echo cannot be successfully removed by a linear acoustic echo canceller. The remaining echo components in the output of acoustic echo suppression (AES) can be further suppressed by applying residual echo suppression (RES) algorithms. In this thesis, we propose an optimal RES gain estimation based on deep neural network (DNN) exploiting both the far-end and the AES output signals in all frequency bins. A DNN structure is introduced as a regression function representing the complex nonlinear mapping from these signals to the optimal RES gain. Because of the capability of the DNN, the spectro-temporal correlations in the full-band can be considered while finding the nonlinear function. The proposed method does not require any explicit double-talk detectors to deal with single-talk and double-talk situations. One of the well-known approaches for nonlinear acoustic echo cancellation is an adaptive Volterra filtering and various algorithms based on the Volterra filter were proposed to describe the characteristics of nonlinear echo and showed the better performance than the conventional linear filtering. However, the performance might be not satisfied since these algorithms could not consider the full correlation for the nonlinear relationship between the input signal and far-end signal in time-frequency domain. In this thesis, we propose a novel DNN-based approach for nonlinear acoustic echo suppression (NAES), extending the proposed RES algorithm. Instead of estimating the residual gain for suppressing the nonlinear echo components, the proposed algorithm straightforwardly recovers the near-end speech signal through the direct gain estimation obtained from DNN frameworks on the input and far-end signal. For echo aware training, a priori and a posteriori signal-to-echo ratio (SER) are introduced as additional inputs of the DNN for tracking the change of the echo signal. In addition, the multi-task learning (MTL) to the DNN-based NAES is combined to the DNN incorporating echo aware training for robustness. In the proposed system, an additional task of double-talk detection is jointly trained with the primary task of the gain estimation for NAES. The DNN can learn the good representations which can suppress more in single-talk periods and improve the gain estimates in double-talk periods through the MTL framework. Besides, the proposed NAES using echo aware training and MTL with double-talk detection makes the DNN be more robust in various conditions. The proposed techniques show significantly better performance than the conventional AES methods in both single- and double-talk periods. As a pre-processing of various applications such as speech recognition and speech enhancement, these approaches can help to transmit the clean speech and provide an acceptable communication in full-duplex real environments.Chapter 1 Introduction 1 1.1 Background 1 1.2 Scope of thesis 3 Chapter 2 Conventional Approaches for Acoustic Echo Suppression 7 2.1 Single Channel Acoustic Echo Cancellation and Suppression 8 2.1.1 Single Channel Acoustic Echo Cancellation 8 2.1.2 Adaptive Filters for Acoustic Echo Cancellation 10 2.1.3 Acoustic Echo Suppression Based on Spectral Modication 11 2.2 Residual Echo Suppression 13 2.2.1 Spectral Feature-based Nonlinear Residual Echo Suppression 15 2.3 Stereophonic Acoustic Echo Cancellation 17 2.4 Wiener Filtering for Stereophonic Acoustic Echo Suppression 20 Chapter 3 Stereophonic Acoustic Echo Suppression Incorporating Spectro-Temporal Correlations 25 3.1 Introduction 25 3.2 Linear Time-Invariant Systems in the STFT Domain with Crossband Filtering 26 3.3 Enhanced SAES (ESAES) Utilizing Spectro-Temporal Correlations 29 3.3.1 Problem Formulation 31 3.3.2 Estimation of Extended PSD Matrices, Echo Spectra, and Gain Function 34 3.3.3 Complexity of the Proposed ESAES Algorithm 36 3.4 Experimental Results 37 3.5 Summary 41 Chapter 4 Nonlinear Residual Echo Suppression Based on Deep Neural Network 43 4.1 Introduction 43 4.2 A Brief Review on RES 45 4.3 Deep Neural Networks 46 4.4 Nonlinear RES using Deep Neural Network 49 4.5 Experimental Results 52 4.5.1 Combination with Stereophonic Acoustic Echo Suppression 59 4.6 Summary 61 Chapter 5 Enhanced Deep Learning Frameworks for Nonlinear Acoustic Echo Suppression 69 5.1 Introduction 69 5.2 DNN-based Nonlinear Acoustic Echo Suppression using Echo Aware Training 72 5.3 Multi-Task Learning for NAES 75 5.4 Experimental Results 78 5.5 Summary 82 Chapter 6 Conclusions 89 Bibliography 91 요약 101Docto

非言語情報の違いに頑健な特徴量表現に着目したニューラルネットワーク音声認識に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 峯松 信明, 東京大学教授 廣瀬 明, 東京大学准教授 鶴岡 慶雅, 東京大学准教授 矢谷 浩司, 国立情報学研究所客員教授 廣瀬 啓吉University of Tokyo(東京大学