13 research outputs found
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
Bidirectional truncated recurrent neural networks for efficient speech denoising
We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.P
Non-Parallel Training in Voice Conversion Using an Adaptive Restricted Boltzmann Machine
In this paper, we present a voice conversion (VC) method that does not use any parallel data while training the model. VC is a technique where only speaker-specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on parallel data-pairs of speech data from the source and target speakers uttering the same sentences. However, the use of parallel data in training causes several problems: 1) the data used for the training are limited to the predefined sentences, 2) the trained model is only applied to the speaker pair used in the training, and 3) mismatches in alignment may occur. Although it is, thus, fairly preferable in VC not to use parallel data, a nonparallel approach is considered difficult to learn. In our approach, we achieve nonparallel training based on a speaker adaptation technique and capturing latent phonological information. This approach assumes that speech signals are produced from a restricted Boltzmann machine-based probabilistic model, where phonological information and speaker-related information are defined explicitly. Speaker-independent and speaker-dependent parameters are simultaneously trained under speaker adaptive training. In the conversion stage, a given speech signal is decomposed into phonological and speaker-related information, the speaker-related information is replaced with that of the desired speaker, and then voice-converted speech is obtained by mixing the two. Our experimental results showed that our approach outperformed another nonparallel approach, and produced results similar to those of the popular conventional Gaussian mixture models-based method that used parallel data in subjective and objective criteria
Automatic speech recognition for European Portuguese
Dissertação de mestrado em Informatics EngineeringThe process of Automatic Speech Recognition (ASR) opens doors to a vast amount of possible
improvements in customer experience. The use of this type of technology has increased
significantly in recent years, this change being the result of the recent evolution in ASR
systems. The opportunities to use ASR are vast, covering several areas, such as medical,
industrial, business, among others. We must emphasize the use of these voice recognition
systems in telecommunications companies, namely, in the automation of consumer assistance
operators, allowing the service to be routed to specialized operators automatically through
the detection of matters to be dealt with through recognition of the spoken utterances. In
recent years, we have seen big technological breakthrough in ASR, achieving unprecedented
accuracy results that are comparable to humans. We are also seeing a move from what
is known as the Traditional approach of ASR systems, based on Hidden Markov Models
(HMM), to the newer End-to-End ASR systems that obtain benefits from the use of deep
neural networks (DNNs), large amounts of data and process parallelization.
The literature review showed us that the focus of this previous work was almost exclusively
for the English and Chinese languages, with little effort being made in the development of
other languages, as it is the case with Portuguese. In the research carried out, we did not
find a model for the European Portuguese (EP) dialect that is freely available for general
use. Focused on this problem, this work describes the development of a End-to-End ASR
system for EP. To achieve this goal, a set of procedures was followed that allowed us to
present the concepts, characteristics and all the steps inherent to the construction of these
types of systems. Furthermore, since the transcribed speech needed to accomplish our goal
is very limited for EP, we also describe the process of collecting and formatting data from a
variety of different sources, most of them freely available to the public. To further try and
improve our results, a variety of different data augmentation techniques were implemented
and tested. The obtained models are based on a PyTorch implementation of the Deep Speech
2 model.
Our best model achieved an Word Error Rate (WER) of 40.5%, in our main test corpus,
achieving slightly better results to those obtained by commercial systems on the same data.
Around 150 hours of transcribed EP was collected, so that it can be used to train other ASR
systems or models in different areas of investigation. We gathered a series of interesting
results on the use of different batch size values as well as the improvements provided by
the use of a large variety of data augmentation techniques. Nevertheless, the ASR theme is vast and there is still a variety of different methods and interesting concepts that we could
research in order to seek an improvement of the achieved results.O processo de Reconhecimento Automático de Fala (ASR) abre portas para uma grande
quantidade de melhorias possíveis na experiência do cliente. A utilização deste tipo de
tecnologia tem aumentado significativamente nos últimos anos, sendo esta alteração o
resultado da evolução recente dos sistemas ASR. As oportunidades de utilização do ASR
são vastas, abrangendo diversas áreas, como médica, industrial, empresarial, entre outras.
É
de realçar que a utilização destes sistemas de reconhecimento de voz nas empresas de
telecomunicações, nomeadamente, na automatização dos operadores de atendimento ao
consumidor, permite o encaminhamento automático do serviço para operadores especializados
através da detecção de assuntos a tratar através do reconhecimento de voz. Nos
últimos anos, vimos um grande avanço tecnológico em ASR, alcançando resultados de
precisão sem precedentes que são comparáveis aos atingidos por humanos. Por outro lado,
vemos também uma mudança do que é conhecido como a abordagem tradicional, baseados
em modelos de Markov ocultos (HMM), para sistemas mais recentes ponta-a-ponta que
reúnem benefícios do uso de redes neurais profundas, em grandes quantidades de dados e
da paralelização de processos.
A revisão da literatura efetuada mostra que o foco do trabalho anterior foi quase que
exclusivamente para as línguas inglesa e chinesa, com pouco esforço no desenvolvimento de
outras línguas, como é o caso do português. Na pesquisa realizada, não encontramos um
modelo para o dialeto português europeu (PE) que se encontre disponível gratuitamente para
uso geral. Focado neste problema, este trabalho descreve o desenvolvimento de um sistema
de ASR ponta-a-ponta para o PE. Para atingir este objetivo, foi seguido um conjunto de
procedimentos que nos permitiram apresentar os conceitos, características e todas as etapas
inerentes à construção destes tipos de sistemas. Além disso, como a fala transcrita necessária
para cumprir o nosso objetivo é muito limitada para PE, também descrevemos o processo
de coleta e formatação desses dados em uma variedade de fontes diferentes, a maioria
delas disponíveis gratuitamente ao público. Para tentar melhorar os nossos resultados, uma
variedade de diferentes técnicas de aumento de dados foram implementadas e testadas. Os
modelos obtidos são baseados numa implementação PyTorch do modelo Deep Speech 2.
O nosso melhor modelo obteve uma taxa de erro de palavras (WER) de 40,5% no nosso
corpus de teste principal, obtendo resultados ligeiramente melhores do que aqueles obtidos
por sistemas comerciais sobre os mesmos dados. Foram coletadas cerca de 150 horas de PE
transcritas, que podem ser utilizadas para treinar outros sistemas ou modelos de ASR em
diferentes áreas de investigação. Reunimos uma série de resultados interessantes sobre o uso de diferentes valores de batch size, bem como as melhorias fornecidas pelo uso de uma
grande variedade de técnicas de data augmentation. O tema ASR é vasto e ainda existe uma
grande variedade de métodos diferentes e conceitos interessantes que podemos investigar
para melhorar os resultados alcançados
Data Augmentation Techniques for Robust Audio Analysis
Having large amounts of training data is necessary for the ever more popular neural networks to perform reliably. Data augmentation, i.e. the act of creating additional training data by performing label-preserving transformations for existing training data, is an efficient solution for this problem. While increasing the amount of data, introducing variations to the data via the transformations also has the power to make machine learning models more robust in real life conditions with noisy environments and mismatches between the training and test data.
In this thesis, data augmentation techniques in audio analysis are reviewed, and a tool for audio data augmentation (TADA) is presented. TADA is capable of performing three audio data augmentation techniques, which are convolution with mobile device microphone impulse responses, convolution with room impulse responses, and addition of background noises. TADA is evaluated by using it in a pronunciation error classification task, where typical pronunciation errors of Finnish people uttering English words are classified. All the techniques are tested first individually and then also in combination.
The experiments are executed with both original and augmented data. In all experiments, using TADA improves the performance of the classifier when compared to training with only original data. Robustness against unseen devices and rooms also improves. Additional gain from performing combined augmentation starts to saturate only after augmenting the training data to 30 times the original amount. Based on the positive impact of TADA for the classification task, it is found that data augmentation with convolutional and additive noises is an effective combination for increasing robustness against environmental distortions and channel effects.Viime aikoina nopeasti yleistyneiden neuroverkkojen opettamiseksi tarvitaan suuria määriä dataa, jotta niistä saadaan luotettavia. Aineiston täydennys, eli lisäaineiston luominen suorittamalla luokkatunnuksen säilyttäviä muunnoksia olemassa olevalle aineistolle, on tehokas ratkaisu kyseiseen ongelmaan. Aineiston kasvattamisen lisäksi vaihteluiden lisääminen opetusdataan voi tehdä koneoppimismalleista robusteja kohinaista, todellista dataa kohtaan.
Tässä työssä käydään läpi äänen analyysissä käytettäviä aineiston täydennysmenetelmiä ja esitellään aineiston lisäämistä varten kehitetty täydennystyökalu. Työkaluun kehitetyt kolme erillistä aineiston täydennysmenetelmää ovat konvoluutio mobiililaitteiden mikrofonien impulssivasteiden kanssa, konvoluutio huoneimpulssivasteiden kanssa sekä taustakohinan lisäys. Työkalua testataan käyttämällä sitä lausumisvirheluokittelutehtävässä, jossa tarkoituksena on luokitella tyypillisiä suomalaisten tekemiä lausumisvirheitä englanninkielisissä sanoissa. Kaikki implementoidut menetelmät testataan aluksi erikseen ja lopuksi yhdessä.
Testit suoritetaan käyttämällä sekä alkuperäistä että täydennettyä testidataa. Kaikissa testeissä työkalua käyttämällä saadaan kasvatettua luokittelijan tarkkuutta verrattuna alkuperäisellä datalla opetettuun luokittelijaan. Robustius uusia mobiililaitteita ja huoneita kohtaan myös paranee. Tarkkuuden kasvu yhdistetyssä testissä saturoituu, kun opetusdata on täydennetty 30-kertaiseksi. Työkalun positiivisen vaikutuksen perusteella aineiston täydennys konvoluutioilla ja lisätyllä kohinalla osoittautuu tehokkaaksi menetelmäksi robustiuden lisäämiseksi ympäristön ja tallennusvälineiden aiheuttamia häiriöitä kohtaan
Model-Based Speech Enhancement
Abstract
A method of speech enhancement is developed that reconstructs clean speech from
a set of acoustic features using a harmonic plus noise model of speech. This is a significant
departure from traditional filtering-based methods of speech enhancement.
A major challenge with this approach is to estimate accurately the acoustic features
(voicing, fundamental frequency, spectral envelope and phase) from noisy speech.
This is achieved using maximum a-posteriori (MAP) estimation methods that operate
on the noisy speech. In each case a prior model of the relationship between the
noisy speech features and the estimated acoustic feature is required. These models
are approximated using speaker-independent GMMs of the clean speech features
that are adapted to speaker-dependent models using MAP adaptation and for noise
using the Unscented Transform.
Objective results are presented to optimise the proposed system and a set of subjective
tests compare the approach with traditional enhancement methods. Threeway
listening tests examining signal quality, background noise intrusiveness and
overall quality show the proposed system to be highly robust to noise, performing
significantly better than conventional methods of enhancement in terms of background
noise intrusiveness. However, the proposed method is shown to reduce signal
quality, with overall quality measured to be roughly equivalent to that of the Wiener
filter