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Combining Speech Attributes for Speech Recognition

This research delves into the development of a system for bottom-up speech recognition using various speech attributes like phonetic attributes and speaker attributes. The study focuses on combining speech attributes effectively to enhance language recognition capabilities, exploring both top-down and bottom-up modeling methodologies.

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Combining Speech Attributes for Speech Recognition

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  1. Combining Speech Attributes for Speech Recognition Jeremy Morris November 9, 2006

  2. Overview • Problem Statement (Motivation) • Conditional Random Fields • Experiments & Results • Future Work

  3. Problem Statement • Developed as part of the ASAT Project • Automatic Speech Attribute Transcription • Project to build tools to extract and parse speech attributes from a speech signal • Goal: Develop a system for bottom-up speech recognition using 'speech attributes'

  4. Speech Attributes? • Any information that could be useful for recognizing the spoken language • Phonetic attributes • Consonants have manner, place of articulation, voicing • Vowels have height, frontness, roundness, tenseness • Speaker attributes (gender, age, etc.) • Any other useful attributes that could be used for speech recognition /d/ manner: stop place of artic: dental voicing: voiced /iy/ height: high frontness: front roundness: nonround tenseness: tense /ae/ height: low frontness: front roundness: nonround tenseness: tense /t/ manner: stop place of artic: dental voicing: unvoiced

  5. Feature Combination • Our piece of this project is to find ways to combine speech attributes together and use them to recognize language • Other groups are working on finding features to extract and methods of extracting them • Note that there is no guarantee that attributes will be independent of each other • In fact, many attributes will be strongly correllated or dependent on other attributes • e.g. voicing for vowels

  6. hyp hyp Top Down Generate a hypothesis See if the data fits the hypothesis Bottom Up Examine the data Search for a hypothesis that fits data data Evidence Combination • Two basic ways to build hypotheses

  7. /iy/ X Top Down • Traditional Automated Speech Recogintion Systems (ASR) use a top-down approach • Hypothesis is the phone we are predicting • Data is some encoding of the acoustic speech signal • A likelihood of the signal given the phone label is learned from data • A prior probability for the phone label is learned from the data • These are combined through Bayes Rule to give us the posterior probability P(label | data) P(/iy/) P(X|/iy/)

  8. /iy/ X Bottom Up • Bottom-up models have the same high-level goal – determine the label from the observation • But instead of a likelihood, the posterior probability P(label | data) is learned directly from the data • Neural Networks can be used to learn probabilities in this manner P(/iy/|X)

  9. /k/ /k/ /iy/ /iy/ /iy/ Speech is a Sequence • Speech is not a single, independent event • It is a combination of multiple events over time • A model to recognize spoken language should take into account dependencies across time

  10. /k/ /k/ /iy/ /iy/ /iy/ X X X X X Speech is a Sequence • A top down model can be extended into a time sequence as a Hidden Markov Model (HMM) • Now our likelihood of the data is over the entire sequence instead of a single phone

  11. Conditional Random Fields • A form of discriminative modelling • Has been used successfully in various domains such as part of speech tagging and other Natural Language Processing tasks • Processes evidence bottom-up • Combines multiple features of the data • Builds the probability P( sequence | data)

  12. Conditional Random Fields • Conceptual Overview • Each attribute of the data we are trying to model fits into a feature function that associates the attribute and a possible label • A positive value if the attribute appears in the data • A zero value if the attribute is not in the data • Each feature function carries a weight that gives the strength of that feature function for the proposed label • High positive weights indicate a good association between the feature and the proposed label • High negative weights indicate a negative association between the feature and the proposed label • Weights close to zero indicate the feature has little or no impact on the identity of the label

  13. Conditional Random Fields /k/ /k/ /iy/ /iy/ /iy/ • CRFs have transition feature functions and state feature functions • Transition functions add associations between transitions from one label to another • State functions help determine the identity of the state X X X X X

  14. State Feature Weight Indicates the strength of the association of this attribute with this label Transition Feature Weight Indicates the strength of the association of this attribute with this transition State Feature Function Association of an attribute with a phone label e.g. f(P(stop), /k/) Transition Feature Function Association of an attribute with a phone-to-phone transition e.g. g(attr, /iy/,/k/) Conditional Random Fields

  15. Experiments • Goal: Implement a Conditional Random Field Model on speech attribute data • Perform phone recognition • Compare results to those obtained via a Tandem system • Experimental Data • TIMIT read speech corpus • Moderate-sized corpus of clean, prompted speech, complete with phonetic-level transcriptions

  16. Attribute Selection • Attribute Detectors • Built using ICSI QuickNet Neural Network software • Two different types of attributes • Phonological feature detectors • Place, Manner, Voicing, Vowel Height, Backness, etc. • Features are grouped into eight classes, with each class having a variable number of possible values based on the IPA phonetic chart • Phone detectors • Neural networks output based on the phone labels – one output per label • Classifiers were trained on 2960 utterances from the TIMIT training set • Uses extracted 12th order PLP coefficients (i.e. frequency coefficients) in a 9 frame window as inputs to the neural networks

  17. Experimental Setup • Code built on the Java CRF toolkit on Sourceforge • http://crf.sourceforge.net • Performs training to maximize the log-likelihood of the training set with respect to the model • Does this via gradient descent – find the place where the gradient of the log-likelihood function goes to zero

  18. Experimental Setup • Output from the Neural Nets are themselves treated as feature functions for the observed sequence • Each attribute/label combination gives us a value for one feature function • We also use a bias feature for each label • Currently, all combinations of features and labels are used as feature functions • e.g. f(P(stop),/t/), f(P(stop),/ae/), etc. • Phone class features are used in the same manner • e.g f(P(/t/), /t/), f(P(/t/), /ae/), etc. • Transition features use only a 0/1 bias feature • 1 if the transition occurs at that timeframe in the training set • 0 if the transition does not occur at that timeframe in the training set • For comparison purposes, we compare to a baseline HMM-trained system that uses decorrellated features as inputs

  19. Initial Results

  20. Experimental Setup • Initial CRF experiments show results comparable to triphone HMM results with only monophone labelling • No decorrellation of features needed • No assumptions about feature independence • Comparison to HMM crippled in one way: • HMM training allowed for shifting of phone boundaries during training • CRF training used set phone boundaries for all training • Another experiment – train the CRF, realign training labels, then retrain on realigned labels

  21. Realignment Results

  22. Experimental Setup • CRFs can also make use of features on the transitions • For the initial experiments, transition feature functions only used bias features (e.g. 1 or 0 based on label in the training corpus) • What if the phone classifications were used as the state features, and the feature classes were used as transition features? • Linguistic observation – feature spreading from phone to phone

  23. Realignment Results

  24. Discussion & Future Work • This seems to be a good model for the type of feature combination we want to perform • Makes use of arbitrary, possibly correllated features • Results on phone recognition task comparable or superior to the alternative sequence model (HMM) • Future Work • New features • What kinds of features can we add to improve our transitions? • We hope to get more from the other research groups • New training methods • Faster algorithms than the gradient descent method exist and need to be tested • Word recogntion • We are thinking about how to model word recogntion in this framework • Larger corpora • TIMIT is a comparably small corpus – we are looking to move to something bigger

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