DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Automatic speech recognition: from study to practice

Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work

Porting concepts from DNNs back to GMMs

Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the 'deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.Comment: Submitted to IEEE Open Journal of Signal Processing. 30 pages, 27 figure

Deep neural networks in acoustic model

L'estudiant m'ha contactat amb el requeriment d'una oferta per matricular-se i aquesta oferta respon a la seva petició. Després de confirmar amb Secretaria Acadèmica que està acceptat a destinació, deixem títol, descripció, objectius, i tutor extern per determinar quan arribi a destí.Do implementation of a training of a deep neural network acoustic model for speech recognitio

Noise adaptive training for subspace Gaussian mixture models

Noise adaptive training (NAT) is an effective approach to normalise the environmental distortions in the training data. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles the problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training. Index Terms: adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture model

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201