WS04 Proposal: Landmark Detection
We propose to develop a landmark-based speech recognizer. We will experiment with the robustness and accuracy benefits provided by (1) diverse acoustic observations, and (2) multi-class label-sequence structural risk minimization. As a preliminary baseline for this proposal, MFCC+d+dd 13-mixture 3-state HMMs were trained to distinguish stops, fricatives, nasals, vowels, glides, and silence. Depending on insertion penalty, six-class recognition accuracy varied from 52% to 74% (82% correct, 8% insertion). Binary recognition accuracy for the four manner-class distinctive features was 80% for [silence], 78% for [syllabic], 78% for [sonorant], and 77% for [continuant]. For this result, the HMM required 6310 trainable parameters. Stevens proposed improving the accuracy and noise robustness of speech recognition by eliminating frame-based observation PDFs, and instead focusing the attention of the classifier on instantaneous phonetic landmarks. Phonetic landmarks are articulatory events with acoustic signatures easily detected at low SNR, including syllabic nuclei, intervocalic glides, and onsets and offsets of the distinctive features [sonorant] and [continuant]. Stevens proposed that acoustic observations should be optimized separately for each landmark type, e.g., measures of signal periodicity sampled once per 10ms for the detection of syllable nuclei, but energies sampled once per 1ms for the detection of stops. Following Stevens’ proposal, Niyogi used kernel-based SVMs to detect stop consonants in TIMIT. EspyWilson and her students further developed SVM-based landmark detectors for onsets and offsets of the distinctive features [silence] (94% recognition accuracy), [syllabic] (79% accuracy), [sonorant] (93%), and [continuant] (94%). Six-manner-class recognition accuracy on TIMIT was 80%, using a total of 160 trainable parameters. Juneja and Espy-Wilson are currently testing a Viterbi-like algorithm for continuous speech recognition using SVM landmark detectors, together with a training algorithm comparable to segmental K-means re-estimation. These results form the foundation for the proposed workshop.