1,240 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
Non-native children speech recognition through transfer learning
This work deals with non-native children's speech and investigates both
multi-task and transfer learning approaches to adapt a multi-language Deep
Neural Network (DNN) to speakers, specifically children, learning a foreign
language. The application scenario is characterized by young students learning
English and German and reading sentences in these second-languages, as well as
in their mother language. The paper analyzes and discusses techniques for
training effective DNN-based acoustic models starting from children native
speech and performing adaptation with limited non-native audio material. A
multi-lingual model is adopted as baseline, where a common phonetic lexicon,
defined in terms of the units of the International Phonetic Alphabet (IPA), is
shared across the three languages at hand (Italian, German and English); DNN
adaptation methods based on transfer learning are evaluated on significant
non-native evaluation sets. Results show that the resulting non-native models
allow a significant improvement with respect to a mono-lingual system adapted
to speakers of the target language
Speaker recognition by means of restricted Boltzmann machine adaptation
Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft
Sampling-based speech parameter generation using moment-matching networks
This paper presents sampling-based speech parameter generation using
moment-matching networks for Deep Neural Network (DNN)-based speech synthesis.
Although people never produce exactly the same speech even if we try to express
the same linguistic and para-linguistic information, typical statistical speech
synthesis produces completely the same speech, i.e., there is no
inter-utterance variation in synthetic speech. To give synthetic speech natural
inter-utterance variation, this paper builds DNN acoustic models that make it
possible to randomly sample speech parameters. The DNNs are trained so that
they make the moments of generated speech parameters close to those of natural
speech parameters. Since the variation of speech parameters is compressed into
a low-dimensional simple prior noise vector, our algorithm has lower
computation cost than direct sampling of speech parameters. As the first step
towards generating synthetic speech that has natural inter-utterance variation,
this paper investigates whether or not the proposed sampling-based generation
deteriorates synthetic speech quality. In evaluation, we compare speech quality
of conventional maximum likelihood-based generation and proposed sampling-based
generation. The result demonstrates the proposed generation causes no
degradation in speech quality.Comment: Submitted to INTERSPEECH 201
From features to speaker vectors by means of restricted Boltzmann machine adaptation
Restricted Boltzmann Machines (RBMs) have shown success in different stages of speaker recognition systems. In this paper, we propose a novel framework to produce a vector-based representation for each speaker, which will be referred to as RBM-vector. This new approach maps the speaker spectral features to a single fixed-dimensional vector carrying speaker-specific information. In this work, a global model, referred to as Universal RBM (URBM), is trained taking advantage of RBM unsupervised learning capabilities. Then, this URBM is adapted
to the data of each speaker in the development, enrolment and
evaluation datasets. The network connection weights of the adapted RBMs are further concatenated and subject to a whitening with dimension reduction stage to build the speaker vectors. The evaluation is performed on the core test condition of the NIST SRE 2006 database, and it is shown that RBM-vectors achieve 15% relative improvement in terms of EER compared to i-vectors using cosine scoring. The score fusion with i-vector attains more than 24% relative improvement. The interest of this result for score fusion yields on the fact that both vectors are produced in an unsupervised fashion and can be used instead of i-vector/PLDA approach, when no data label is available. Results obtained for RBM-vector/PLDA framework is comparable with the ones from i-vector/PLDA. Their score fusion achieves 14% relative improvement compared to i-vector/PLDA.Peer ReviewedPostprint (published version
Articulatory features for speech-driven head motion synthesis
This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin
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