Overview ► Recall ► What are sound features? ► Feature detection and extraction ► Features in Sphinx III

Recall: ► Speech signal is ‘slowly’ time varying singnal ► There are a number of linguistically distinct speech sounds (phonemes) in a language. ► It is possible to represent the sound spectrogram in a 3D spectrogram of the speech intensity and the different frequency bands over time ► Most SR systems rely heavily on vowel recognition to achieve high performance (they are long in duration and spectrally well defined and therefore easily recognized)

Speech sounds and features Examples: ► Vowels (a, u, …) ► Diphthongs (f.i. a y as in guy, … ) ► Semivowels (w, l, r, y) ► Nasal Consonants (m, n) ► Unvoiced Fricatives (f, s) ► Voiced Fricatives (v, th, z) ► Voiced and Unvoiced Stops (b, d, g) ► They all have their own characteristics (features)

ASR Stages 1) speech analysis system: to provide an appropriate spectral representation of the characteristics of the time-varying speech signal  2) feature detection stage: to convert the spectral measurements to a set of features that describe the broad acoustic properties of the different phonetic units (f.i. nasality, frication, formant locations, voiced-unvoiced classification, ratios of high- and low-frequency energy, etc.) 3) segmentation and labeling phase: to find stable regions and then label the segmented region according to how well the features within that region match those of individual phonetic units 4) final output of the recognizer is the word or word sequence that best matches

Feature detection (and extraction) ► Speech segment contains certain characteristics, features. ► Different segments of speech contain different features, specific for the kind of segment! ► Goal is to try to classify a speech segment into one of several broad speech classes (f.i. via binary tree: compact/diffuse, acute/grave, long/short, high/low frequency, etc) ► Ideally, feature vectors for a given word should hopefully be the same regardless of the way in which the word has been uttered

Last week: Mel-Frequency Ceptrum Coefficient ► ► Fourier Transform extracts the frequency components of a signal in the time domain ► ► Frequency domain is filtered/sliced in 12 smaller parts, where for each it’s own coefficient (MFCC) can be calculated ► MFCC's use the log-spectrum of the speech signal. The logarithmic nature of the technique is significant since the human auditory system perceives sound on a logarithmic scale above certain frequencies

Fourier Transform Fourier Transform Cepstral Analysis Cepstral Analysis Perceptual Weighting Perceptual Weighting Time Derivative Time Derivative Time Derivative Time Derivative Energy + Mel-Spaced Cepstrum Delta Energy + Delta Cepstrum Delta-Delta Energy + Delta-Delta Cepstrum Input Speech MFCC’s are beautiful, because they incorporate knowledge of the nature of speech sounds in measurement of the features. Utilize rudimentary models of human perception. Acoustic Modeling: Feature Extraction Fourier Transform time domain  frequency domain Frequency domain is sliced in 12 smaller parts with each it’s own MFCC Include absolute energy and 12 spectral measurements. Time derivatives to model spectral change

What ‘to do’ with the MFCC’s: ► A speech recognizer can be built using the energy values (time domain) and 12 MFCC's (frequency domain), plus the first and second order derivatives of those coefficients. 13 (Absolute Energy (1) and MFCCs (12)) 13 (Delta First-order derivatives of the 13 absolute coefficients) 13 (Delta-Delta Second-order derivatives of the 13 absolute coefficients) Total Basic MFCC Front End ► The derivatives are useful because they provide information about the ► The derivatives are useful because they provide information about the spectral change ► These total of 39 coefficients will provide information about the different features in that segment! ► The feature measurements of the segments are stored in so called ‘feature vectors’, that can be used in the next stage of the speech recognition (f.i. Hidden Markov Model)

In Sphinx III: computation of feature vectors ► feat_s2mfc2feat ► feat_s2mfc2feat_block 1. MFC file is read 2. Initialization: defining the kind of input->feature conversion desired (there are some differences between Sphinx II and Sphinx III) 3. Feature vectors are computed for the entire segment specified (feat_s2mfc2feat and feat_s2mfc2feat_block) In Sphinx in the feature vectors, the streams of features are stored as follows: ► CEP: C1-C12 ► DCEP: D1-D12 ► Energy values: C0, D0, DD0 ► D2CEP: DD1-DD12

► So, at this point in the speech recognition process, you have stored feature vectors for the entire speech segment you are looking at, providing the necessary information about what kind features are in that segment. ► Now, ► Now, The feature stream can be analyzed using a Hidden-Markov Model (HMM) frication burst voicing round nasal glide a1a2:a5a6a1a2:a5a6 … … “one” “two” “oh” …… … … :::: Feature Extraction Modules Input speech Feature Vector Concat. Train The feature stream is analyzed using a Hidden-Markov Model (HMM)