68 research outputs found
Analysis of Unsupervised and Noise-Robust Speaker-Adaptive HMM-Based Speech Synthesis Systems toward a Unified ASR and TTS Framework
For the 2009 Blizzard Challenge we have built an unsupervised version of the HTS-2008 speaker-adaptive HMM-based speech synthesis system for English, and a noise robust version of the systems for Mandarin. They are designed from a multidisciplinary application point of view in that we attempt to integrate the components of the TTS system with other technologies such as ASR. All the average voice models are trained exclusively from recognized, publicly available, ASR databases. Multi-pass LVCSR and confidence scores calculated from confusion network are used for the unsupervised systems, and noisy data recorded in cars or public spaces is used for the noise robust system. We believe the developed systems form solid benchmarks and provide good connections to ASR fields. This paper describes the development of the systems and reports the results and analysis of their evaluation
Incorporating Weak Statistics for Low-Resource Language Modeling
Automatic speech recognition (ASR) requires a strong language model to guide the acoustic model and favor likely utterances. While many tasks enjoy billions of language model training tokens, many domains which require ASR do not have readily available electronic corpora.The only source of useful language modeling data is expensive and time-consuming human transcription of in-domain audio. This dissertation seeks to quickly and inexpensively improve low-resource language modeling for use in automatic speech recognition.
This dissertation first considers efficient use of non-professional human labor to best improve system performance, and demonstrate that it is better to collect more data, despite higher transcription error, than to redundantly transcribe data to improve quality. In the process of developing procedures to collect such data, this work also presents an efficient rating scheme to detect poor transcribers without gold standard data.
As an alternative to this process, automatic transcripts are generated with an ASR system and explore efficiently combining these low-quality transcripts with a small amount of high quality transcripts. Standard n-gram language models are sensitive to the quality of the highest order n-gram and are unable to exploit accurate weaker statistics. Instead, a log-linear language model is introduced, which elegantly incorporates a variety of background models through MAP adaptation. This work introduces marginal class constraints which effectively capture knowledge of transcriber error and improve performance over n-gram features.
Finally, this work constrains the language modeling task to keyword search of words unseen in the training text. While overall system performance is good, these words suffer the most due to a low probability in the language model. Semi-supervised learning effectively extracts likely n-grams containing these new keywords from a large corpus of audio. By using a search metric that favors recall over precision, this method captures over 80% of the potential gain
Automatic role recognition
The computing community is making significant efforts towards the development of automatic approaches for the analysis of social interactions. The way people interact depends on the context, but there is one aspect that all social interactions seem to have in common: humans behave according to roles. Therefore, recognizing the roles of participants is an essential step towards understanding social interactions and the construction of socially aware
computer.
This thesis addresses the problem of automatically recognizing roles of participants in multi-party recordings. The objective is to assign to each participant a role. All the proposed approaches use a similar strategy. They all start by segmenting the audio into turns. Those
turns are used as basic analysis units. The next step is to extract features accounting for the organization of turns. The more sophisticated approaches extend the features extracted with features from either the prosody or the semantic. Finally, the mapping of people or turns to
roles is done using statistical models. The goal of this thesis is to gain a better understanding of role recognition and we will investigate three aspects that can influence the performance
of the system:
We investigate the impact of modelling the dependency between the roles.
We investigate the contribution of different modalities for the effectiveness of role
recognition approach.
We investigate the effectiveness of the approach for different scenarios.
Three models are proposed and tested on three different corpora totalizing more than 90 hours of audio. The first contribution of this thesis is to investigate the combination of turn-taking features and semantic information for role recognition, improving the accuracy of
role recognition from a baseline of 46.4% to 67.9% on the AMI meeting corpus. The second contribution is to use features extracted from the prosody to assign roles. The performance of this model is 89.7% on broadcast news and 87.0% on talk-shows. Finally, the third contribution is the development of a model robust to change in the social setting. This model achieved an accuracy of 86.7% on a database composed of a mixture of broadcast news and
talk-shows
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Joint Training Methods for Tandem and Hybrid Speech Recognition Systems using Deep Neural Networks
Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-art automatic speech recognition (ASR) systems over the
past few decades. Recently, due to the rapid development of deep learning technologies, deep neural networks (DNNs) have become an essential part of nearly all kinds of ASR approaches. Among HMM-based ASR approaches, DNNs are most commonly used to extract features (tandem system configuration) or to directly produce HMM output probabilities (hybrid system configuration).
Although DNN tandem and hybrid systems have been shown to have superior
performance to traditional ASR systems without any DNN models, there are still
issues with such systems. First, some of the DNN settings, such as the choice of
the context-dependent (CD) output targets set and hidden activation functions, are
usually determined independently from the DNN training process. Second, different
ASR modules are separately optimised based on different criteria following a greedy
build strategy. For instance, for tandem systems, the features are often extracted by a
DNN trained to classify individual speech frames while acoustic models are built upon
such features according to a sequence level criterion. These issues mean that the best performance is not theoretically guaranteed.
This thesis focuses on alleviating both issues using joint training methods. In DNN
acoustic model joint training, the decision tree HMM state tying approach is extended
to cluster DNN-HMM states. Based on this method, an alternative CD-DNN training
procedure without relying on any additional system is proposed, which can produce
DNN acoustic models comparable in word error rate (WER) with those trained by the
conventional procedure. Meanwhile, the most common hidden activation functions,
the sigmoid and rectified linear unit (ReLU), are parameterised to enable automatic
learning of function forms. Experiments using conversational telephone speech (CTS)
Mandarin data result in an average of 3.4% and 2.2% relative character error rate (CER) reduction with sigmoid and ReLU parameterisations. Such parameterised functions can also be applied to speaker adaptation tasks.
At the ASR system level, DNN acoustic model and corresponding speaker dependent (SD) input feature transforms are jointly learned through minimum phone error
(MPE) training as an example of hybrid system joint training, which outperforms the
conventional hybrid system speaker adaptive training (SAT) method. MPE based speaker independent (SI) tandem system joint training is also studied. Experiments on
multi-genre broadcast (MGB) English data show that this method gives a reduction
in tandem system WER of 11.8% (relative), and the resulting tandem systems are
comparable to MPE hybrid systems in both WER and the number of parameters. In
addition, all approaches in this thesis have been implemented using the hidden Markov model toolkit (HTK) and the related source code has been or will be made publicly available with either recent or future HTK releases, to increase the reproducibility of the work presented in this thesis.Cambridge International Scholarship, Cambridge Overseas Trust
Research funding, EPSRC Natural Speech Technology Project
Research funding, DARPA BOLT Program
Research funding, iARPA Babel Progra
A review of natural language processing in contact centre automation
Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco
Robust learning of acoustic representations from diverse speech data
Automatic speech recognition is increasingly applied to new domains. A key challenge is
to robustly learn, update and maintain representations to cope with transient acoustic
conditions. A typical example is broadcast media, for which speakers and environments
may change rapidly, and available supervision may be poor. The concern of this
thesis is to build and investigate methods for acoustic modelling that are robust to the
characteristics and transient conditions as embodied by such media.
The first contribution of the thesis is a technique to make use of inaccurate transcriptions as supervision for acoustic model training. There is an abundance of audio
with approximate labels, but training methods can be sensitive to label errors, and their
use is therefore not trivial. State-of-the-art semi-supervised training makes effective
use of a lattice of supervision, inherently encoding uncertainty in the labels to avoid
overfitting to poor supervision, but does not make use of the transcriptions. Existing
approaches that do aim to make use of the transcriptions typically employ an algorithm
to filter or combine the transcriptions with the recognition output from a seed model,
but the final result does not encode uncertainty. We propose a method to combine the
lattice output from a biased recognition pass with the transcripts, crucially preserving
uncertainty in the lattice where appropriate. This substantially reduces the word error
rate on a broadcast task.
The second contribution is a method to factorise representations for speakers and
environments so that they may be combined in novel combinations. In realistic scenarios,
the speaker or environment transform at test time might be unknown, or there may be
insufficient data to learn a joint transform. We show that in such cases, factorised, or
independent, representations are required to avoid deteriorating performance. Using
i-vectors, we factorise speaker or environment information using multi-condition training
with neural networks. Specifically, we extract bottleneck features from networks trained
to classify either speakers or environments. The resulting factorised representations
prove beneficial when one factor is missing at test time, or when all factors are seen,
but not in the desired combination.
The third contribution is an investigation of model adaptation in a longitudinal
setting. In this scenario, we repeatedly adapt a model to new data, with the constraint
that previous data becomes unavailable. We first demonstrate the effect of such a
constraint, and show that using a cyclical learning rate may help. We then observe
that these successive models lend themselves well to ensembling. Finally, we show
that the impact of this constraint in an active learning setting may be detrimental to
performance, and suggest to combine active learning with semi-supervised training to
avoid biasing the model.
The fourth contribution is a method to adapt low-level features in a parameter-efficient and interpretable manner. We propose to adapt the filters in a neural feature
extractor, known as SincNet. In contrast to traditional techniques that warp the
filterbank frequencies in standard feature extraction, adapting SincNet parameters is
more flexible and more readily optimised, whilst maintaining interpretability. On a task
adapting from adult to child speech, we show that this layer is well suited for adaptation
and is very effective with respect to the small number of adapted parameters
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
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
Social Network Analysis for Automatic Role Recognition
The computing community has shown a significant interest for the analysis of social interactions in the last decade. Different aspects of social interactions have been studied such as dominance, emotions, conflicts, etc. However, the recognition of roles has been neglected whereas these are a key aspect of social interactions. In fact, sociologists have shown not only that people play roles each time they interact, but also that roles shape behavior and expectations of interacting participants. The aim of this thesis is to fill this gap by investigating the problem of automatic role recognition in a wide range of interaction settings, including production environments, e.g. news and talk-shows, and spontaneous exchanges, e.g. meetings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). The importance of roles has been assessed in an application scenario as well. In particular, the thesis shows that roles help to segment talk-shows into stories, i.e. time intervals during which a single topic is discussed, with satisfactory performance. The main contributions of this thesis are as follows: To the best of our knowledge, this is the first work where social network analysis is applied to automatic analysis of conversation recordings. This thesis provides the first quantitative measure of how much roles constrain conversations, and a large corpus of recordings annotated in terms of roles. The results of this work have been published in one journal paper, and in five conference articles
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