29 research outputs found
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