Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Discriminative and adaptive training for robust speech recognition and understanding

Robust automatic speech recognition (ASR) and understanding (ASU) under various conditions remains to be a challenging problem even with the advances of deep learning. To achieve robust ASU, two discriminative training objectives are proposed for keyword spotting and topic classification: (1) To accurately recognize the semantically important keywords, the non-uniform error cost minimum classification error training of deep neural network (DNN) and bi-directional long short-term memory (BLSTM) acoustic models is proposed to minimize the recognition errors of only the keywords. (2) To compensate for the mismatched objectives of speech recognition and understanding, minimum semantic error cost training of the BLSTM acoustic model is proposed to generate semantically accurate lattices for topic classification. Further, to expand the application of the ASU system to various conditions, four adaptive training approaches are proposed to improve the robustness of the ASR under different conditions: (1) To suppress the effect of inter-speaker variability on speaker-independent DNN acoustic model, speaker-invariant training is proposed to learn a deep representation in the DNN that is both senone-discriminative and speaker-invariant through adversarial multi-task training (2) To achieve condition-robust unsupervised adaptation with parallel data, adversarial teacher-student learning is proposed to suppress multiple factors of condition variability in the procedure of knowledge transfer from a well-trained source domain LSTM acoustic model to the target domain. (3) To further improve the adversarial learning for unsupervised adaptation with unparallel data, domain separation networks are used to enhance the domain-invariance of the senone-discriminative deep representation by explicitly modeling the private component that is unique to each domain. (4) To achieve robust far-field ASR, an LSTM adaptive beamforming network is proposed to estimate the real-time beamforming filter coefficients to cope with non-stationary environmental noise and dynamic nature of source and microphones positions.Ph.D

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Multi-dialect Arabic broadcast speech recognition

Dialectal Arabic speech research suffers from the lack of labelled resources and standardised orthography. There are three main challenges in dialectal Arabic speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training robust dialectal speech recognition models from limited labelled data and (iii) evaluating speech recognition for dialects with no orthographic rules. This thesis is concerned with the following three contributions: Arabic Dialect Identification: We are mainly dealing with Arabic speech without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently diverse to the extent that one can argue that they are different languages rather than dialects of the same language. We have two contributions: First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected from Al Jazeera TV channel. We obtained utterance level dialect labels for 57 hours of high-quality consisting of four major varieties of dialectal Arabic (DA), comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification (ADI) system. We explored two main groups of features, namely acoustic features and linguistic features. For the linguistic features, we look at a wide range of features, addressing words, characters and phonemes. With respect to acoustic features, we look at raw features such as mel-frequency cepstral coefficients combined with shifted delta cepstra (MFCC-SDC), bottleneck features and the i-vector as a latent variable. We studied both generative and discriminative classifiers, in addition to deep learning approaches, namely deep neural network (DNN) and convolutional neural network (CNN). In our work, we propose Arabic as a five class dialect challenge comprising of the previously mentioned four dialects as well as modern standard Arabic. Arabic Speech Recognition: We introduce our effort in building Arabic automatic speech recognition (ASR) and we create an open research community to advance it. This section has two main goals: First, creating a framework for Arabic ASR that is publicly available for research. We address our effort in building two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast news using more than 1,200 hours of speech and 130M words of text collected from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre data with limited non-orthographic speech collected from YouTube, with special attention paid to transfer learning. Second, building a robust Arabic ASR system and reporting a competitive word error rate (WER) to use it as a potential benchmark to advance the state of the art in Arabic ASR. Our overall system is a combination of five acoustic models (AM): unidirectional long short term memory (LSTM), bidirectional LSTM (BLSTM), time delay neural network (TDNN), TDNN layers along with LSTM layers (TDNN-LSTM) and finally TDNN layers followed by BLSTM layers (TDNN-BLSTM). The AM is trained using purely sequence trained neural networks lattice-free maximum mutual information (LFMMI). The generated lattices are rescored using a four-gram language model (LM) and a recurrent neural network with maximum entropy (RNNME) LM. Our official WER is 13%, which has the lowest WER reported on this task. Evaluation: The third part of the thesis addresses our effort in evaluating dialectal speech with no orthographic rules. Our methods learn from multiple transcribers and align the speech hypothesis to overcome the non-orthographic aspects. Our multi-reference WER (MR-WER) approach is similar to the BLEU score used in machine translation (MT). We have also automated this process by learning different spelling variants from Twitter data. We mine automatically from a huge collection of tweets in an unsupervised fashion to build more than 11M n-to-m lexical pairs, and we propose a new evaluation metric: dialectal WER (WERd). Finally, we tried to estimate the word error rate (e-WER) with no reference transcription using decoding and language features. We show that our word error rate estimation is robust for many scenarios with and without the decoding features

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors. To address these issues, a set of compact and data efficient speaker-dependent (SD) parameter representations are used to facilitate both speaker adaptive training and test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR systems. The sensitivity to supervision quality is reduced using a confidence score-based selection of the less erroneous subset of speaker-level adaptation data. Two lightweight confidence score estimation modules are proposed to produce more reliable confidence scores. The data sparsity issue, which is exacerbated by data selection, is addressed by modelling the SD parameter uncertainty using Bayesian learning. Experiments on the benchmark 300-hour Switchboard and the 233-hour AMI datasets suggest that the proposed confidence score-based adaptation schemes consistently outperformed the baseline speaker-independent (SI) Conformer model and conventional non-Bayesian, point estimate-based adaptation using no speaker data selection. Similar consistent performance improvements were retained after external Transformer and LSTM language model rescoring. In particular, on the 300-hour Switchboard corpus, statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute (9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin

THE MICROSOFT 2016 CONVERSATIONAL SPEECH RECOGNITION SYSTEM

ABSTRACT We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Self-supervised learning for automatic speech recognition In low-resource environments

Supervised deep neural networks trained with substantial amounts of annotated speech data have demonstrated impressive performance across a spectrum of spoken language processing applications, frequently establishing themselves as the leading models in respective competitions. Nonetheless, a significant challenge arises from the heavy reliance on extensive annotated data for training these systems. This reliance poses a significant scalability limitation, hindering the continual enhancement of state-of-the-art performance. Moreover, it presents a more fundamental obstacle for deploying deep neural networks in speech-related domains where acquiring labeled data is inherently arduous, expensive, or time-intensive, which are considered as low-resource ASR problems in this thesis. Unlike annotated speech data, collecting untranscribed audio is typically more cost-effective. In this thesis, we investigate the application of self-supervised learning in low-resource tasks, a learning approach where the learning objective is derived directly from the input data itself. We employ this method to harness the scalability and affordability of untranscribed audio resources in problems where we do not have enough training data, with the goal of enhancing the performance of spoken language technology. In particular, we propose three self-supervised methodologies. One model is based on the concept of two-fine-tuning steps, while the other two revolve around the notion of identifying an improved hidden unit. These approaches are designed to learn contextualized speech representations from speech data lacking annotations. We demonstrate the capacity of our self-supervised techniques to learn representations that convert the higher-level characteristics of speech signals more effectively than conventional acoustic features. Additionally, we present how these representations enhance the performance of deep neural networks on ASR tasks with limited resources. Beyond introducing novel learning algorithms, we conduct in-depth analyses to comprehend the properties of the acquired self-supervised representations and elucidate the distinct design elements that separate one self-supervised model from another

Adaptation of speech recognition systems to selected real-world deployment conditions

Tato habilitační práce se zabývá problematikou adaptace systémů rozpoznávání řeči na vybrané reálné podmínky nasazení. Je koncipována jako sborník celkem dvanácti článků, které se touto problematikou zabývají. Jde o publikace, jejichž jsem hlavním autorem nebo spoluatorem, a které vznikly v rámci několika navazujících výzkumných projektů. Na řešení těchto projektů jsem se podílel jak v roli člena výzkumného týmu, tak i v roli řešitele nebo spoluřešitele. Publikace zařazené do tohoto sborníku lze rozdělit podle tématu do tří hlavních skupin. Jejich společným jmenovatelem je snaha přizpůsobit daný rozpoznávací systém novým podmínkám či konkrétnímu faktoru, který významným způsobem ovlivňuje jeho funkci či přesnost. První skupina článků se zabývá úlohou neřízené adaptace na mluvčího, kdy systém přizpůsobuje svoje parametry specifickým hlasovým charakteristikám dané mluvící osoby. Druhá část práce se pak věnuje problematice identifikace neřečových událostí na vstupu do systému a související úloze rozpoznávání řeči s hlukem (a zejména hudbou) na pozadí. Konečně třetí část práce se zabývá přístupy, které umožňují přepis audio signálu obsahujícího promluvy ve více než v jednom jazyce. Jde o metody adaptace existujícího rozpoznávacího systému na nový jazyk a metody identifikace jazyka z audio signálu. Obě zmíněné identifikační úlohy jsou přitom vyšetřovány zejména v náročném a méně probádaném režimu zpracování po jednotlivých rámcích vstupního signálu, který je jako jediný vhodný pro on-line nasazení, např. pro streamovaná data.This habilitation thesis deals with adaptation of automatic speech recognition (ASR) systems to selected real-world deployment conditions. It is presented in the form of a collection of twelve articles dealing with this task; I am the main author or a co-author of these articles. They were published during my work on several consecutive research projects. I have participated in the solution of them as a member of the research team as well as the investigator or a co-investigator. These articles can be divided into three main groups according to their topics. They have in common the effort to adapt a particular ASR system to a specific factor or deployment condition that affects its function or accuracy. The first group of articles is focused on an unsupervised speaker adaptation task, where the ASR system adapts its parameters to the specific voice characteristics of one particular speaker. The second part deals with a) methods allowing the system to identify non-speech events on the input, and b) the related task of recognition of speech with non-speech events, particularly music, in the background. Finally, the third part is devoted to the methods that allow the transcription of an audio signal containing multilingual utterances. It includes a) approaches for adapting the existing recognition system to a new language and b) methods for identification of the language from the audio signal. The two mentioned identification tasks are in particular investigated under the demanding and less explored frame-wise scenario, which is the only one suitable for processing of on-line data streams

Self-supervised learning for automatic speech recognition In low-resource environments

Supervised deep neural networks trained with substantial amounts of annotated speech data have demonstrated impressive performance across a spectrum of spoken language processing applications, frequently establishing themselves as the leading models in respective competitions. Nonetheless, a significant challenge arises from the heavy reliance on extensive annotated data for training these systems. This reliance poses a significant scalability limitation, hindering the continual enhancement of state-of-the-art performance. Moreover, it presents a more fundamental obstacle for deploying deep neural networks in speech-related domains where acquiring labeled data is inherently arduous, expensive, or time-intensive, which are considered as low-resource ASR problems in this thesis. Unlike annotated speech data, collecting untranscribed audio is typically more cost-effective. In this thesis, we investigate the application of self-supervised learning in low-resource tasks, a learning approach where the learning objective is derived directly from the input data itself. We employ this method to harness the scalability and affordability of untranscribed audio resources in problems where we do not have enough training data, with the goal of enhancing the performance of spoken language technology. In particular, we propose three self-supervised methodologies. One model is based on the concept of two-fine-tuning steps, while the other two revolve around the notion of identifying an improved hidden unit. These approaches are designed to learn contextualized speech representations from speech data lacking annotations. We demonstrate the capacity of our self-supervised techniques to learn representations that convert the higher-level characteristics of speech signals more effectively than conventional acoustic features. Additionally, we present how these representations enhance the performance of deep neural networks on ASR tasks with limited resources. Beyond introducing novel learning algorithms, we conduct in-depth analyses to comprehend the properties of the acquired self-supervised representations and elucidate the distinct design elements that separate one self-supervised model from another

Speech recognition with probabilistic transcriptions and end-to-end systems using deep learning

In this thesis, we develop deep learning models in automatic speech recognition (ASR) for two contrasting tasks characterized by the amounts of labeled data available for training. In the first half, we deal with scenarios when there are limited or no labeled data for training ASR systems. This situation is commonly prevalent in languages which are under-resourced. However, in the second half, we train ASR systems with large amounts of labeled data in English. Our objective is to improve modern end-to-end (E2E) ASR using attention modeling. Thus, the two primary contributions of this thesis are the following: Cross-Lingual Speech Recognition in Under-Resourced Scenarios: A well-resourced language is a language with an abundance of resources to support the development of speech technology. Those resources are usually defined in terms of 100+ hours of speech data, corresponding transcriptions, pronunciation dictionaries, and language models. In contrast, an under-resourced language lacks one or more of these resources. The most expensive and time-consuming resource is the acquisition of transcriptions due to the difficulty in finding native transcribers. The first part of the thesis proposes methods by which deep neural networks (DNNs) can be trained when there are limited or no transcribed data in the target language. Such scenarios are common for languages which are under-resourced. Two key components of this proposition are Transfer Learning and Crowdsourcing. Through these methods, we demonstrate that it is possible to borrow statistical knowledge of acoustics from a variety of other well-resourced languages to learn the parameters of a the DNN in the target under-resourced language. In particular, we use well-resourced languages as cross-entropy regularizers to improve the generalization capacity of the target language. A key accomplishment of this study is that it is the first to train DNNs using noisy labels in the target language transcribed by non-native speakers available in online marketplaces. End-to-End Large Vocabulary Automatic Speech Recognition: Recent advances in ASR have been mostly due to the advent of deep learning models. Such models have the ability to discover complex non-linear relationships between attributes that are usually found in real-world tasks. Despite these advances, building a conventional ASR system is a cumbersome procedure since it involves optimizing several components separately in a disjoint fashion. To alleviate this problem, modern ASR systems have adopted a new approach of directly transducing speech signals to text. Such systems are known as E2E systems and one such system is the Connectionist Temporal Classification (CTC). However, one drawback of CTC is the hard alignment problem as it relies only on the current input to generate the current output. In reality, the output at the current time is influenced not only by the current input but also by inputs in the past and the future. Thus, the second part of the thesis proposes advancing state-of-the-art E2E speech recognition for large corpora by directly incorporating attention modeling within the CTC framework. In attention modeling, inputs in the current, past, and future are distinctively weighted depending on the degree of influence they exert on the current output. We accomplish this by deriving new context vectors using time convolution features to model attention as part of the CTC network. To further improve attention modeling, we extract more reliable content information from a network representing an implicit language model. Finally, we used vector based attention weights that are applied on context vectors across both time and their individual components. A key accomplishment of this study is that it is the first to incorporate attention directly within the CTC network. Furthermore, we show that our proposed attention-based CTC model, even in the absence of an explicit language model, is able to achieve lower word error rates than a well-trained conventional ASR system equipped with a strong external language model