In an earlier study, we proposed a very effective dialect/accent classification algorithm, which is named Word based Dialect Classification (WDC). The WDC works well for large size corpora and significantly outperforms traditional Large Vocabulary Continuous Speech Recognition (LVCSR) based systems, which is claimed to be the best performing system for language identification. For a small train...

The goal of acoustic Language Identification (LID) is to identify the language of spoken utterances. The described system is based on parallel Hidden Markov Model (HMM) phoneme recognizers. The standard approach for parameter learning of Hidden Markov Model parameters is Maximum Likelihood (ML) estimation which is not directly related to the classification error rate. Based on the Minimum Class...

In this report, we propose a discriminative decoder for the recognition of phoneme sequences, i.e. the identification of the uttered phoneme sequence from a speech recording. This task is solved as a 3 step process: a phoneme classifier first classifies each accoustic frame, then temporal consistency features (TCF) are extracted from the phoneme classifier outputs, and finally a sequence decode...

We present a method for audio-visual classification of Swedish phonemes, to be used in computer-assisted pronunciation training. The probabilistic kernel-based method is applied to the audio signal and/or either a principal or an independent component (PCA or ICA) representation of the mouth region in video images. We investigate which representation (PCA or ICA) that may be most suitable and t...

This work explores the potential for robust classification of phonemes in the presence of additive noise and linear filtering using high-dimensional features in the subbands of acoustic waveforms. The proposed technique is compared with state-of-the-art automatic speech recognition (ASR) front-ends on the TIMIT phoneme classification task using support vector machines (SVMs). The key issues of ...

This paper proposes a phoneme lattice based TextTiling approach towards multilingual story segmentation. The phoneme is the smallest segmental unit in a language and the number of phonemes in a language is usually far smaller than the number of words. Furthermore, many phonemes are shared by different languages. These properties make phonemes particularly appropriate for representing multilingu...

We present a method for phoneme recognition using an expert system combining spectrogram reading knowledge and neural networks, and we report its performance. The proposed expert system consists of two parts : (1) phoneme segmentation based on spectrogram reading knowledge used by human experts, and (2) phoneme identification using neural networks applied to the phoneme boundaries determined in...

This work focuses on the robustness of phoneme classification to additive noise in the acoustic waveform domain using support vector machines (SVMs). We address the issue of designing kernels for acoustic waveforms which imitate the state-ofthe-art representations such as PLP and MFCC and are tuned to the physical properties of speech. For comparison, classification results in the PLP represent...

This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors’ intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quan...