593 research outputs found
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification
Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization
Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
Bio-motivated features and deep learning for robust speech recognition
Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech
Recognition (ASR) technologies has experienced over the last five years
their performance under hard environmental condition is still far from
that of humans preventing their adoption in several real applications.
In this thesis the challenge of robustness of modern automatic speech
recognition systems is addressed following two main research lines.
The first one focuses on modeling the human auditory system to
improve the robustness of the feature extraction stage yielding to novel
auditory motivated features. Two main contributions are produced.
On the one hand, a model of the masking behaviour of the Human
Auditory System (HAS) is introduced, based on the non-linear filtering
of a speech spectro-temporal representation applied simultaneously
to both frequency and time domains. This filtering is accomplished
by using image processing techniques, in particular mathematical
morphology operations with an specifically designed Structuring Element
(SE) that closely resembles the masking phenomena that take
place in the cochlea. On the other hand, the temporal patterns of
auditory-nerve firings are modeled. Most conventional acoustic features
are based on short-time energy per frequency band discarding
the information contained in the temporal patterns. Our contribution
is the design of several types of feature extraction schemes based on
the synchrony effect of auditory-nerve activity, showing that the modeling
of this effect can indeed improve speech recognition accuracy in
the presence of additive noise. Both models are further integrated into
the well known Power Normalized Cepstral Coefficients (PNCC).
The second research line addresses the problem of robustness in
noisy environments by means of the use of Deep Neural Networks
(DNNs)-based acoustic modeling and, in particular, of Convolutional
Neural Networks (CNNs) architectures. A deep residual network
scheme is proposed and adapted for our purposes, allowing Residual
Networks (ResNets), originally intended for image processing tasks,
to be used in speech recognition where the network input is small
in comparison with usual image dimensions. We have observed that
ResNets on their own already enhance the robustness of the whole system
against noisy conditions. Moreover, our experiments demonstrate
that their combination with the auditory motivated features devised
in this thesis provide significant improvements in recognition accuracy
in comparison to other state-of-the-art CNN-based ASR systems
under mismatched conditions, while maintaining the performance in
matched scenarios.
The proposed methods have been thoroughly tested and compared
with other state-of-the-art proposals for a variety of datasets and
conditions. The obtained results prove that our methods outperform
other state-of-the-art approaches and reveal that they are suitable for
practical applications, specially where the operating conditions are
unknown.El objetivo de esta tesis se centra en proponer soluciones al problema
del reconocimiento de habla robusto; por ello, se han llevado a cabo
dos líneas de investigación.
En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento
del sistema auditivo humano, modelando especialmente los fenómenos
de enmascaramiento y sincronía. En la segunda, se propone mejorar
las tasas de reconocimiento mediante el uso de técnicas de
aprendizaje profundo, en conjunto con las características propuestas.
Los métodos propuestos tienen como principal objetivo, mejorar la
precisión del sistema de reconocimiento cuando las condiciones de
operación no son conocidas, aunque el caso contrario también ha sido
abordado.
En concreto, nuestras principales propuestas son los siguientes:
Simular el sistema auditivo humano con el objetivo de mejorar
la tasa de reconocimiento en condiciones difíciles, principalmente
en situaciones de alto ruido, proponiendo esquemas de
extracción de características novedosos.
Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación:
• Modelar el comportamiento de enmascaramiento del sistema
auditivo humano, usando técnicas del procesado de
imagen sobre el espectro, en concreto, llevando a cabo el
diseño de un filtro morfológico que captura este efecto.
• Modelar el efecto de la sincroní que tiene lugar en el nervio
auditivo.
• La integración de ambos modelos en los conocidos Power
Normalized Cepstral Coefficients (PNCC).
La aplicación de técnicas de aprendizaje profundo con el objetivo
de hacer el sistema más robusto frente al ruido, en particular
con el uso de redes neuronales convolucionales profundas, como
pueden ser las redes residuales.
Por último, la aplicación de las características propuestas en
combinación con las redes neuronales profundas, con el objetivo
principal de obtener mejoras significativas, cuando las condiciones
de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ
Environmentally robust ASR front-end for deep neural network acoustic models
This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.csl.2014.11.00
ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION
Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria.
Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal.
Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system
Methods for speaking style conversion from normal speech to high vocal effort speech
This thesis deals with vocal-effort-focused speaking style conversion (SSC). Specifically, we studied two topics on conversion of normal speech to high vocal effort. The first topic involves the conversion of normal speech to shouted speech. We employed this conversion in a speaker recognition system with vocal effort mismatch between test and enrollment utterances (shouted speech vs. normal speech). The mismatch causes a degradation of the system's speaker identification performance. As solution, we proposed a SSC system that included a novel spectral mapping, used along a statistical mapping technique, to transform the mel-frequency spectral energies of normal speech enrollment utterances towards their counterparts in shouted speech. We evaluated the proposed solution by comparing speaker identification rates for a state-of-the-art i-vector-based speaker recognition system, with and without applying SSC to the enrollment utterances. Our results showed that applying the proposed SSC pre-processing to the enrollment data improves considerably the speaker identification rates.
The second topic involves a normal-to-Lombard speech conversion. We proposed a vocoder-based parametric SSC system to perform the conversion. This system first extracts speech features using the vocoder. Next, a mapping technique, robust to data scarcity, maps the features. Finally, the vocoder synthesizes the mapped features into speech. We used two vocoders in the conversion system, for comparison: a glottal vocoder and the widely used STRAIGHT. We assessed the converted speech from the two vocoder cases with two subjective listening tests that measured similarity to Lombard speech and naturalness. The similarity subjective test showed that, for both vocoder cases, our proposed SSC system was able to convert normal speech to Lombard speech. The naturalness subjective test showed that the converted samples using the glottal vocoder were clearly more natural than those obtained with STRAIGHT
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