Improving Speech Recognition Using a Data-Driven Approach

In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the well-known template matching technique, DTW. This approach allows us to better capture the temporal dynamics of the speech signal while avoiding some of the HMM assumptions such as the piecewise stationarity. Potentially, such data-driven techniques also allow us to better exploit meta-data and environmental information, such as speaker, gender, accent and noise conditions. However, we cannot entirely abandon HMMs, which are very powerful and scalable models. Thus, we investigate one way to combine and take advantage of both the approaches, combining scores of HMMs and reference templates. Experiments on the Numbers95 database showed that this combination yields 22\% relative improvement in word error rate over the baseline HMM performance. Applying K-means clustering to the acoustic vectors speeds up the decoding, while still retaining a significant improvement in the recognition accuracy

Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning

In this work, we investigated the teacher-student training paradigm to train a fully learnable multi-channel acoustic model for far-field automatic speech recognition (ASR). Using a large offline teacher model trained on beamformed audio, we trained a simpler multi-channel student acoustic model used in the speech recognition system. For the student, both multi-channel feature extraction layers and the higher classification layers were jointly trained using the logits from the teacher model. In our experiments, compared to a baseline model trained on about 600 hours of transcribed data, a relative word-error rate (WER) reduction of about 27.3% was achieved when using an additional 1800 hours of untranscribed data. We also investigated the benefit of pre-training the multi-channel front end to output the beamformed log-mel filter bank energies (LFBE) using L2 loss. We find that pre-training improves the word error rate by 10.7% when compared to a multi-channel model directly initialized with a beamformer and mel-filter bank coefficients for the front end. Finally, combining pre-training and teacher-student training produces a WER reduction of 31% compared to our baseline.Comment: To appear in ICASSP 202

A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s

On the application of reservoir computing networks for noisy image recognition

Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project