Learning the Inter-frame Distance for Discriminative Template-based Keyword Detection

This paper proposes a discriminative approach to template-based keyword detection. We introduce a method to learn the distance used to compare acoustic frames, a crucial element for template matching approaches. The proposed algorithm estimates the distance from data, with the objective to produce a detector maximizing the Area Under the receiver operating Curve (AUC), i.e. the standard evaluation measure for the keyword detection problem. The experiments performed over a large corpus, SpeechDatII, suggest that our model is effective compared to an HMM system, e.g. the proposed approach reaches 93.8\% of averaged AUC compared to 87.9\% for the HMM

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

Implementation and Evaluation of Acoustic Distance Measures for Syllables

Munier C. Implementation and Evaluation of Acoustic Distance Measures for Syllables. Bielefeld (Germany): Bielefeld University; 2011.In dieser Arbeit werden verschiedene akustische Ähnlichkeitsmaße für Silben motiviert und anschließend evaluiert. Der Mahalanobisabstand als lokales Abstandsmaß für einen Dynamic-Time-Warping-Ansatz zum Messen von akustischen Abständen hat die Fähigkeit, Silben zu unterscheiden. Als solcher erlaubt er die Klassifizierung von Silben mit einer Genauigkeit, die für die Klassifizierung von kleinen akustischen Einheiten üblich ist (60 Prozent für eine Nächster-Nachbar-Klassifizierung auf einem Satz von zehn Silben für Samples eines einzelnen Sprechers). Dieses Maß kann durch verschiedene Techniken verbessert werden, die jedoch seine Ausführungsgeschwindigkeit verschlechtern (Benutzen von mehr Mischverteilungskomponenten für die Schätzung von Kovarianzen auf einer Gaußschen Mischverteilung, Benutzen von voll besetzten Kovarianzmatrizen anstelle von diagonalen Kovarianzmatrizen). Durch experimentelle Evaluierung wird deutlich, dass ein gut funktionierender Algorithmus zur Silbensegmentierung, welcher eine akkurate Schätzung von Silbengrenzen erlaubt, für die korrekte Berechnung von akustischen Abständen durch die in dieser Arbeit entwickelten Ähnlichkeitsmaße unabdingbar ist. Weitere Ansätze für Ähnlichkeitsmaße, die durch ihre Anwendung in der Timbre-Klassifizierung von Musikstücken motiviert sind, zeigen keine adäquate Fähigkeit zur Silbenunterscheidung.In this work, several acoustic similarity measures for syllables are motivated and successively evaluated. The Mahalanobis distance as local distance measure for a dynamic time warping approach to measure acoustic distances is a measure that is able to discriminate syllables and thus allows for syllable classification with an accuracy that is common to the classification of small acoustic units (60 percent for a nearest neighbor classification of a set of ten syllables using samples of a single speaker). This measure can be improved using several techniques that however impair the execution speed of the distance measure (usage of more mixture density components for the estimation of covariances from a Gaussian mixture model, usage of fully occupied covariance matrices instead of diagonal covariance matrices). Through experimental evaluation it becomes evident that a decently working syllable segmentation algorithm allowing for accurate syllable border estimations is essential to the correct computation of acoustic distances by the similarity measures developed in this work. Further approaches for similarity measures which are motivated by their usage in timbre classification of music pieces do not show adequate syllable discrimination abilities

A locally weighted distance measure for example based speech recognition

State-of-the-art speech recognition relies on a state-dependent distance measure. In HMM systems, the distance measure is trained into state-dependent covariance matrices using a maximum likelihood or discriminative criterion. This “automatic” adjustment of the distance measure is traditionally considered an inherent advantage of HMMs over DTW recognizers, as those typically rely on a uniform Euclidean distance. In this paper we show how to incorporate a non-uniform weighted distance measure into an example based recognition system. By doing so we manage to combine the superior segmental behaviour of DTW with the near-optimal acoustic distance measure as found in HMMs. The non-uniform distance measure enforces modifications to the k nearest neighbours search, an essential component in our large vocabulary DTW approach. We show that the complexity of our solution remains within bounds. The validity of the full approach is verified by experimental results on the Resource Management and TIDigits tasks.De Wachter M., Demuynck K., Wambacq P., Van Compernolle D., "A locally weighted distance measure for example based speech recognition", Proceedings IEEE international conference on acoustics, speech, and signal processing - ICASSP'2004, vol. I, pp. 181-184, May 17-21, 2004, Montréal, Quebec, Canada.status: publishe