Long-Running Speech Recognizer:An End-to-End Multi-Task Learning Framework for Online ASR and VAD

When we use End-to-end automatic speech recognition (E2E-ASR) system for real-world applications, a voice activity detection (VAD) system is usually needed to improve the performance and to reduce the computational cost by discarding non-speech parts in the audio. This paper presents a novel end-to-end (E2E), multi-task learning (MTL) framework that integrates ASR and VAD into one model. The proposed system, which we refer to as Long-Running Speech Recognizer (LR-SR), learns ASR and VAD jointly from two seperate task-specific datasets in the training stage. With the assistance of VAD, the ASR performance improves as its connectionist temporal classification (CTC) loss function can leverage the VAD alignment information. In the inference stage, the LR-SR system removes non-speech parts at low computational cost and recognizes speech parts with high robustness. Experimental results on segmented speech data show that the proposed MTL framework outperforms the baseline single-task learning (STL) framework in ASR task. On unsegmented speech data, we find that the LR-SR system outperforms the baseline ASR systems that build an extra GMM-based or DNN-based voice activity detector.Comment: 5 pages, 2 figure

Speech enhancement using deep learning

This thesis explores the possibility to achieve enhancement on noisy speech signals using Deep Neural Networks. Signal enhancement is a classic problem in speech processing. In the last years, researches using deep learning has been used in many speech processing tasks since they have provided very satisfactory results. As a first step, a Signal Analysis Module has been implemented in order to calculate the magnitude and phase of each audio file in the database. The signal is represented into its magnitude and its phase, where the magnitude is modified by the neural network, and then it is reconstructed with the original phase. The implementation of the Neural Networks is divided into two stages.The first stage was the implementation of a Speech Activity Detection Deep Neural Network (SAD-DNN). The magnitude previously calculated, applied to the noisy data, will train the SAD-DNN in order to classify each frame in speech or non-speech. This classification is useful for the network that does the final cleaning. The Speech Activity Detection Deep Neural Network is followed by a Denoising Auto-Encoder (DAE). The magnitude and the label speech or non-speech will be the input of this second Deep Neural Network in charge of denoising the speech signal. The first stage is also optimized to be adequate for the final task in this second stage. In order to do the training, Neural Networks require datasets. In this project the Timit corpus [9] has been used as dataset for the clean voice (target) and the QUT-NOISE TIMIT corpus[4] as noisy dataset (source). Finally, Signal Synthesis Module reconstructs the clean speech signal from the enhanced magnitudes and the phase. In the end, the results provided by the system have been analysed using both objective and subjective measures.Esta tesis explora la posibilidad de conseguir mejorar señales de voz con ruido utilizando Redes Neuronales Profundas. La mejora de señales es un problema clásico del procesado de señal, pero recientemente se esta investigando con deep learning, ya que son técnicas que han dado resultados muy satisfactorios en muchas tareas del procesado de señal. Como primer paso, se ha implementado un Módulo de Análisis de Señal con el objetivo de extraer el módulo y fase de cada archivo de voz de la base de datos. La señal se representa en módulo y fase, donde el módulo se modifica con la red neuronal y posteriormente se reconstruye con la fase original. La implementación de la Red Neuronal consta de dos etapas. En la primera etapa se implementó una Red Neuronal de Detección de Actividad de Voz. El módulo previamente calculado, aplicado a los datos con ruido, se utiliza como entrada para entrenar esta red, de manera que se consigue clasificar cada trama en voz o no voz. Esta clasificación es útil para la red que se encarga de hacer la limpieza. A continuación de la Red Neuronal de Detección de Actividad de Voz se implementa otra, con el objetivo de eliminar el ruido. El módulo junto con la etiqueta obtenida en la red anterior serán la entrada de esta nueva red. En esta segunda etapa también se optimiza la primera para adaptarse a la tarea final. Las Redes Neuronales requieren bases de datos para el entrenamiento. En este proyecto se ha utilizado el Timit corpus [9] como base de datos de voz limpia (objetivo) y el QUT-NOISE TIMIT [4] como base de datos con ruido (fuente). A continuación, el Módulo de Síntesis de Señal reconstruye la señal de voz limpia a partir del módulo sin ruido y la fase original.Aquesta tesis explora la possibilitat d'aconseguir millorar senyals de veu amb soroll, utilitzant Xarxes Neuronals Profundes. La millora de senyals és un problema clàssic del processat de senyal, però recentment s'està investigant amb deep learning, ja que són tècniques que han donat resultats molt satisfactoris en moltes tasques de processament de veu. Com a primer pas, s'ha implementat un Mòdul d'Anàlisi de Senyal amb l'objectiu d'extreure el mòdul i la fase de cada arxiu d'àudio de la base de dades. El senyal es representa en mòdul i fase, on el mòdul es modifica amb la xarxa neuronal i posteriorment es reconstrueix amb la fase original. La implementació de les Xarxes Neuronals consta de dues etapes. En la primera etapa es va implementar una Xarxa Neuronal de Detecció d'Activitat de Veu. El mòdul prèviament calculat, aplicat a les dades amb soroll, s'utilitza com entrada per entrenar aquesta xarxa, de manera que s'aconsegueix classificar cada trama en veu o no veu. Aquesta classificació és útil per la xarxa que fa la neteja final. A continuació de la Xarxa Neuronal de Detecció d'Activitat de Veu s'implementa una altra amb l'objectiu d'eliminar el soroll. El mòdul, juntament amb la etiqueta obtinguda en la xarxa anterior, seran l'entrada d'aquesta nova xarxa. En aquesta segona etapa també s'optimitza la primera per adaptar-se a la tasca final. Les Xarxes Neuronals requereixen bases de dades per fer l'entrenament. En aquest projecte s'ha utilitzat el Timit corpus [9] com a base de dades de veu neta (objectiu) i el QUT-NOISE TIMIT[4] com a base de dades amb soroll (font). A continuació, el Mòdul de Síntesi de Senyal reconstrueix el senyal de veu net a partir del mòdul netejat i la fase original. Finalment, els resultats obtinguts del sistema van ser analitzats utilitzant mesures objectives i subjectives

End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models

Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea proposing a \emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 hours of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio

BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition

Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech recognition. There are two key features in BridgeNet. First, BridgeNet extends traditional student-teacher frameworks by providing multiple hints from a teacher network. Hints are not limited to the soft labels from a teacher network. Teacher's intermediate feature representations can better guide a student network to learn how to denoise or dereverberate noisy input. Second, the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance. The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem, where it achieved up to 13.24% relative WER reductions on AMI corpus compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work