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Machine Learning in Spoken Language
Processing
Lecture 21
Spoken Language Processing
Prof. Andrew Rosenberg
2
Spoken Language Processing
• How machines can interact with speech.
• Speech Recognition• Speech Synthesis• Analysis of Speech
• Lexical Processing• Acoustic Signal Processing
3
Processing Tools
• In many applications, a common trajectory can be observed.
– Manually written rule-based systems
– Corpus-based Analysis
– Automatic training of systems via Machine Learning
4
Rule based systems
• Expert knowledge of a domain
• Often based on un-tested hypotheses
• Brittle– These are difficult to modify– Often have complex interdependencies– Rarely are able to determine
“confidence”
5
Machine Learning Systems
• Learn the relationship between– A Feature Vector, and – A dependent variable {label, or
number}
Classifier
Training data
Learning Algorithm
Classifier
HypothesisFeature Vector
Feature Vectors
Labels
6
Where do we use machine learning?
• The trajectory in natural language processing and speech has been from– manually written rules, to– automatically generated rules learned
from an abundance of data
• Speech Recognition• Speech Synthesis• Prosodic Analysis• Segmentation• Grapheme to
phoneme conversion• Speech act
classification• Disfluency
Identification• Emotion classification• Speech segmentation• Part of speech
tagging
• Parsing• Translation• Turn-taking• Information
Extraction
7
How do we use machine learning
• The Standard Approach to learning– Identify labeled training data– Decide what to label – syllables or words– Extract aggregate acoustic features
based on the labeling region– Train a supervised classifier– Evaluate using cross-validation or a
held-out test set.
8
What’s the role of linguistics?
• How do the rule based systems inform machine learning?
• Feature Representations.– The way we represent an entity or
phenomenon is informed by intuitions and prior study.
– The process of hand generating rules has moved to hand generation of Feature Extraction methods
9
What are the favorite tools in SLP?
• Decision Trees• Support Vector Machines
– Conditional Random Fields
• Neural Networks• Hidden Markov Model• k-means• k-nearest neighbors
• Graphical Models• Expectation maximization
10
Training, Development and Testing
• Available data is commonly divided into three sets– Training
• Used to train the model
– Development• Used to learn the best settings for
parameters
– Testing• Used to evaluate the performance of the
model trained on the training data with parameters l
11
Cross-Validation
• Cross Validation is a technique to estimate the generalization performance of a classifier.
• Identify n “folds” of the available data.• Train on n-1 folds• Test on the remaining fold.• Calculate average performance
…
12
Stratified Cross-validation
• Some classes have skewed distributions– For example, parts of speech.
• When creating cross validation folds, the class distribution is maintained across all folds
Function Noun Verb Adj.
13
Dimensionality
• In general, the more dimensions that a feature vector has, the more training data is necessary for reliable learning.– Some classifiers are more sensitive to
this than others.
• When we have a vocabulary of size N, this is often converted to N binary variables.
• This can quickly lead to an enormous feature space.
14
Dimensionality Reduction techniques
• Dimensionality reduction techniques are commonly used to reduce the number of dimensions, while keeping as much information as possible
• Regularization• Principle Components Analysis• Multi-dimensional scaling• Quantization
15
Next Time
• Working Session• Anonymous Course Feedback