Efficient Posterior Exemplar Search Space Hashing Exploiting Class-Specific Sparsity Structures

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 quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small subset of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of similar exemplars. The $k$ nearest neighbor ($k$NN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phoneme classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of $k$NN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

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 quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small number of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of neighboring exemplars. The $k$ nearest neighbor ($k$NN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phonetic classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of $k$NN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

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 quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small number of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of neighboring exemplars. The $k$ nearest neighbor ($k$NN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phonetic classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of $k$NN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

State of the art query by example spoken term detection (QbE-STD) systems rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN). The posteriors are often used for pattern matching or dynamic time warping (DTW). Exploiting posterior probabilities as speech representation propounds diverse advantages in a classification system. One key property of the posterior representations is that they admit a highly effective hashing strategy that enables indexing the large archive in divisions for reducing the search complexity. Moreover, posterior indexing leads to a compressed representation and enables pronunciation dewarping and partial detection with no need for DTW. We exploit these characteristics of the posterior space in the context of redundant hash addressing for query-by-example spoken term detection (QbE-STD). We evaluate the QbE-STD system on AMI corpus and demonstrate that tremendous speedup and superior accuracy is achieved compared to the state-of-the-art pattern matching and DTW solutions. The system has great potential to enable massively large scale query detection

Sound Pattern Matching for Automatic Prosodic Event Detection

Prosody in speech is manifested by variations of loudness, exaggeration of pitch, and specific phonetic variations of prosodic segments. For example, in the stressed and unstressed syllables, there are differences in place or manner of articulation, vowels in unstressed syllables may have a more central articulation, and vowel reduction may occur when a vowel changes from a stressed to an unstressed position. In this paper, we characterize the sound patterns using phonological posteriors to capture the phonetic variations in a concise manner. The phonological posteriors quantify the posterior probabilities of the phonological features given the input speech acoustics, and they are obtained using the deep neural network (DNN) computational method. Built on the assumption that there are unique sound patterns in different prosodic segments, we devise a sound pattern matching (SPM) method based on 1-nearest neighbour classifier. In this work, we focus on automatic detection of prosodic stress placed on words, called also emphasized words. We evaluate the SPM method on English and French data with emphasized words. The word emphasis detection works very well also on cross-lingual tests, that is using a French classifier on English data, and vice versa

Sparse Pronunciation Codes for Perceptual Phonetic Information Assessment

Speech is a complex signal produced by a highly constrained articulation machinery. Neuro and psycholinguistic theories assert that speech can be decomposed into molecules of structured atoms. Although characterization of the atoms is controversial, the experiments support the notion of invariant speech codes governing speech production and perception. We exploit deep neural network (DNN) invariant representation learning for probabilistic characterization of the phone attributes defined in terms of the phonological classes and known as the smallest-size perceptual categories. We cast speech perception as a channel for phoneme information transmission via the phone attributes. Structured sparse codes are identified from the phonological probabilities for natural speech pronunciation. We exploit the sparse codes in information transmission analysis for assessment of phoneme pronunciation. The linguists define a single binary phonological code per phoneme. In contrast, probabilistic estimation of the phonological classes enables us to capture large variation in structures of speech pronunciation. Hence, speech assessment may not be confined to the single expert knowledge based mapping between phoneme and phonological classes and it may be extended to multiple data-driven mappings observed in natural speech

PAoS Markers: Trajectory Analysis of Selective Phonological Posteriors for Assessment of Progressive Apraxia of Speech

Progressive apraxia of Speech (PAoS) is a progressive motor speech disorder associated with neurodegenerative disease causing impairment of phonetic encoding and motor speech planning. Clinical observation and acoustic studies show that duration analysis provides reliable cues for diagnosis of the disease progression and severity of articulatory disruption. The goal of this paper is to develop computational methods for objective evaluation of duration and trajectory of speech articulation. We use phonological posteriors as speech features. Phonological posteriors consist of probabilities of phonological classes estimated for every short segment of the speech signal. PAoS encompasses lengthening of duration which is more pronounced in vowels; we thus hypothesize that a small subset of phonological classes provide stronger evidence for duration and trajectory analysis. These classes are determined through analysis of linear prediction coefficients (LPC). To enable trajectory analysis without phonetic alignment, we exploit phonological structures defined through quantization of phonological posteriors. Duration and trajectory analysis are conducted on blocks of multiple consecutive segments possessing similar phonological structures. Moreover, unique phonological structures are identified for every severity condition