Conditional Random Fields for ASR Jeremy Morris May 5, 2006

Overview Problem Statement (Motivation) Conditional Random Fields Experiments Attribute Selection Experimental Setup Results Future Work

Problem Statement Developed as part of the ASAT Project (Automatic Speech Attribute Transcription) Goal: Develop a system for bottom-up speech recognition using 'speech attributes'

Speech Attributes? Any information that could be useful for recognizing the spoken language Phonetic attributes Speaker attributes (gender, age, etc.) Any other useful attributes that could be used for speech recognition Note that there is no guarantee that attributes will be independent of each other One part of this project is to explore ways to create a framework for easily combining new features for experimental purposes /d/ manner: stop place of artic: dental voicing: voiced /iy/ height: high backness: front roundness: nonround /t/ manner: stop place of artic: dental voicing: unvoiced

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

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 /iy/ X P(/iy/) P(X|/iy/)

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 is learned from the data Neural Networks have been used to learn these probabilities /iy/ X P(/iy/|X)

Speech is a Sequence Speech is not a single, independent event /k/ /iy/ 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

Speech is a Sequence /k/ /iy/ X A top down (generative) 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

Speech is a Sequence /k/ /iy/ Y Tandem is a method for using evidence bottom up (discriminative) Hypothesis output of Neural Network is used to train an HMM Not a pure discriminative method, but a combination of generative and discriminative methods X X X

Bottom up Modelling The idea is to have a system that combines evidence layer by layer Speech attributes contribute to phone attribute detection Phone attributes contribute to “syllable” attribute detection, and so on Each layer combines information from previous layers to form its hypotheses We want to do this probabalistically – no hard decisions

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)

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

Conditional Random Fields /k/ /k/ /iy/ /iy/ /iy/ X X X X X 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

Conditional Random Fields State Feature Weight λ=10 One possible weight value for this state feature (Strong) Transition Feature Weight μ=4 One possible weight value for this transition feature State Feature Function f([x is stop], /t/) One possible state feature function For our attributes and labels Transition Feature Function g(x, /iy/,/k/) One possible transition feature function Indicates /k/ followed by /iy/

Experiments Goal: Implement a Conditional Random Field Model on ASAT-style 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

Attribute Selection Attribute Detectors ICSI QuickNet Neural Networks 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 applied to 2960 utterances from the TIMIT training set

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 Uses a Limited Memory BGFS algorithm to minimize the gradient of the log-likelihood For CRF models, maximizing the log-likelihood of the empirical distribution of the data as predicted by the model is the same as maximizing the entropy (Berger et. al.)

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 Note that this makes the feature functions non-binary features.

Results Model Phone Accuracy Phone Correctness Tandem (phones) 67.32% 73.81% CRF (phones) 66.89% 68.49% Tandem (features) 66.85% 72.42% CRF (features) 63.84% 65.45% CRF (phones/feas) 67.87% 69.47%

Future Work More features Tuning Word recogntion Other corpora What kinds of features can we add to improve our transitions? Tuning HMM model has parameters that can be tuned for better performance – can we tweak the CRF to do something similar? Word recogntion How does this model do at the full word recognition level, instead of just phones Other corpora Can we extend this method beyond TIMIT to different types of corpora? (e.g. WSJ)