Detection and Recognition of Number Sequences in Spoken Utterances

In this paper we investigate the detection and recognition of sequences of numbers in spoken utterances. This is done in two steps: first, the entire utterance is decoded assuming that only numbers were spoken. In the second step, non-number segments (garbage) are detected based on word confidence measures. We compare this approach to conventional garbage models. Also, a comparison of several phone posterior based confidence measures is presented in this paper. The work is evaluated in terms of detection task (hit rate and false alarms) and recognition task (word accuracy) within detected number sequences. The proposed method is tested on German continuous spoken utterances where target content (numbers) is only 20\%

Posterior features applied to speech recognition tasks with user-defined vocabulary

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Posterior-Based Features and Distances in Template Matching for Speech Recognition

The use of large speech corpora in example-based approaches for speech recognition is mainly focused on increasing the number of examples. This strategy presents some difficulties because databases may not provide enough examples for some rare words. In this paper we present a different method to incorporate the information contained in such corpora in these example-based systems. A multilayer perceptron is trained on these databases to estimate speaker and task-independent phoneme posterior probabilities, which are used as speech features. By reducing the variability of features, fewer examples are needed to properly characterize a word. In this way, performance can be highly improved when limited number of examples is available. Moreover, we also study posterior-based local distances, these result more effective than traditional Euclidean distance. Experiments on Phonebook database support the idea that posterior features with a proper local distance can yield competitive results

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

An Acoustic Model Based on Kullback-Leibler Divergence for Posterior Features

This paper investigates the use of features based on posterior probabilities of subword units such as phonemes. These features are typically transformed when used as inputs for a hidden Markov model with mixture of Gaussians as emission distribution (HMM/GMM). In this work, we introduce a novel acoustic model that avoids the Gaussian assumption and directly uses posterior features without any transformation. This model is described by a finite state machine where each state is characterized by a target distribution and the cost function associated to each state is given by the Kullback-Leibler (KL) divergence between its target distribution and the posterior features. Furthermore, hybrid HMM/ANN system can be seen as a particular case of this KL-based model where state target distributions are predefined. A training method is also presented that minimizes the KL-divergence between the state target distributions and the posteriors features

Using Posterior-Based Features in Template Matching for Speech Recognition

Given the availability of large speech corpora, as well as the increasing of memory and computational resources, the use of template matching approaches for automatic speech recognition (ASR) have recently attracted new attention. In such template-based approaches, speech is typically represented in terms of acoustic vector sequences, using spectral-based features such as MFCC of PLP, and local distances are usually based on Euclidean or Mahalanobis distances. In the present paper, we further investigate template-based ASR and show (on a continuous digit recognition task) that the use of posterior-based features significantly improves the standard template-based approaches, yielding to systems that are very competitive to state-of-the-art HMMs, even when using a very limited number (e.g., 10) of reference templates. Since those posteriors-based features can also be interpreted as a probability distribution, we also show that using Kullback-Leibler (KL) divergence as a local distance further improves the performance of the template-based approach, now beating state-of-the-art of more complex posterior-based HMMs systems (usually referred to as "Tandem")

Using RASTA in task independent TANDEM feature extraction

In this work, we investigate the use of RASTA filter in the TANDEM feature extraction method when trained with a task independent data. RASTA filter removes the linear distortion introduced by the communication channel which is demonstrated in a 18\% relative improvement on the Numbers 95 task. Also, studies yielded a relative improvement of 35\% over the basic PLP features by combining TANDEM features and conventional PLP features

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

In a previous paper on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templates to the test utterance. Traditionally, this selection is given by a distortion measure on the acoustic features. In this work, we propose to improve this template selection with the use of meta-linguistic information as prior knowledge. In this way, similarity is not only based on acoustic features but also on other sources of information that are present in the speech signal. Results on a continuous digit recognition task confirm the statement that similarity between words does not only depend on acoustic features since we obtained 24\% relative improvement over the baseline. Interestingly, results are better even when compared to a system with no prior information but a larger number of templates

On Joint Modelling of Grapheme and Phoneme Information using KL-HMM for ASR

In this paper, we propose a simple approach to jointly model both grapheme and phoneme information using Kullback-Leibler divergence based HMM (KL-HMM) system. More specifically, graphemes are used as subword units and phoneme posterior probabilities estimated at output of multilayer perceptron are used as observation feature vector. Through preliminary studies on DARPA Resource Management corpus it is shown that although the proposed approach yield lower performance compared to KL-HMM system using phoneme as subword units, this gap in the performance can be bridged via temporal modelling at the observation feature vector level and contextual modelling of early tagged contextual graphemes