Learning An Invariant Speech Representation

Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input. We formulate the problem of finding robust speech features for supervised learning with small sample complexity as a problem of learning representations of the signal that are maximally invariant to intraclass transformations and deformations. We propose an extension of a theory for unsupervised learning of invariant visual representations to the auditory domain and empirically evaluate its validity for voiced speech sound classification. Our version of the theory requires the memory-based, unsupervised storage of acoustic templates -- such as specific phones or words -- together with all the transformations of each that normally occur. A quasi-invariant representation for a speech segment can be obtained by projecting it to each template orbit, i.e., the set of transformed signals, and computing the associated one-dimensional empirical probability distributions. The computations can be performed by modules of filtering and pooling, and extended to hierarchical architectures. In this paper, we apply a single-layer, multicomponent representation for phonemes and demonstrate improved accuracy and decreased sample complexity for vowel classification compared to standard spectral, cepstral and perceptual features.Comment: CBMM Memo No. 022, 5 pages, 2 figure

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

Histogram Equalization-Based Features for Speech, Music, and Song Discrimination

In this letter, we present a new class of segment-based features for speech, music and song discrimination. These features, called PHEQ (Polynomial-Fit Histogram Equalization), are derived from the nonlinear relationship between the short-term feature distributions computed at segment level and a reference distribution. Results show that PHEQ characteristics outperform short-term features such as Mel Frequency Cepstrum Coefficients (MFCC) and conventional segment-based ones such as MFCC mean and variance. Furthermore, the combination of short-term and PHEQ features significantly improves the performance of the whole system

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes