Carnegie Mellon's Harpy System

Sherry Yu-Ting

Wansu

Janice

Wenjun Meng

Carnegie Mellon's Harpy System

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Introduction

Harpy System is a result of combing and improving the best features from two speech systems previously developed at Carnegie Mellon University: Hearsay-I ( Erman 1974 ) and Dragon ( Baker 1975 ). Two feature of the Harpy system that led to a successful demonstration are its representation of knowledge and the use of new search techniques. In developing speech recognition systems, it is necessary to devise a means of acquiring and representing the many diverse types of knowledge that characterize speech. We must also develop matching and searching techniques that convert this passive knowledge into an active process for understanding the utterance in the presence of error, noise, and uncertainty.

When an utterance is input to the computer. Several pre-processing steps are taken to prepare the data for recognition. The utterance is segmented into acoustic units and analyzed to determine the segmented features and parameters. The goal of recognition tasks is to find an optimal sequence of phones satisfying two criteria: the sequence must represent a legal path through the knowledge network and should consist of phones with high acoustic matches.

Harpy Systems uses a beam search to locate this optimal sequence of phones. Harpy begins the beam search by taking all legal phones from the start of the sentence from the knowledge network and entering them in a recognition tree. Next, a path probability is calculated for each candidate. The path with the best probability is determined and remaining candidates are compared with it. Those that fall below a threshold of acceptability are eliminated from further searching. The successors of the surviving candidates are expanded based on the information in the knowledge network.

	Hearsay System	Dragon System	Harpy System
Sources of Knowledge	Acoustic-Phonetics (the characteristics of the sounds) Prosodics (the stress and intonation patterns of speech) Lexicon (a dictionary of legal words) Syntax (the grammatical structure of the language) Semantics (the meaning of words and sentences) Pragmatics (the context of the conversation)	Almost no speech dependent heuristics. The integrated knowledge of both syntax and lexical spellings, a set of user dependent acoustic-phonetic templates, and an acoustic-phonetic probability matching routine. Mathematical acceptability available ---one of probabilistic function of Markov Process.	The combination of speech dependent heuristics ( Hearsay-I System ) and mathematical tractable model ( Dragon System ). This leverages the use of heuristics to reduce the search space therefore speed increase. The system extensively use a network that represents both all legal syntactic paths and pronunciations of these legal paths. No a-priori transition probabilities.
Knowledge Representation	Procedural embedding.	Markove networks.	Transition networks.
Search Strategy	Best-First with backtracking.	All paths in parallel ( search all the possible syntactic and acoustic paths through the network in parallel to determine optimal path ) with no backtracking.	''best few'' in parallel with no backtracking. Beam Search Locates optimal solution Examines near-miss alternatives No backtracking
Segmentation	Yes ( uses it to reduce effective utterance length )	No	Yes
Strength	Segmentation of the acoustic signal can effectively reduce the amount of speech data searched. Heuristic speech knowledge serves as a practical guide to mapping and rating words.	The network data representation of combined knowledge of syntax and the phonetic dictionary spellings is tractable. The dynamic programming scheme for searching all the network paths in parallel guarantees both a recognition will always be achieved and that the recognition will be the globally optimum one. ( with the given model )	N/A
Weakness	Best-first strategy involves backtracking, which can be costly in large search space.	Searching all paths ( even in parallel ) is time consuming.	N/A

• simple intro of the relationship between hearsay-I, Dragon system, and harpy system
• general info about how harpy system works (features, methods
• simply mention the advatages of harpy sytem

Historical Context

• detailed info about hearsay-I and dragon

Hearsay System

• The Hearsay System must use all these sources of knowledge effectively in the Hearsay System, this is achieved by representing knowledge as as a set of cooperating parallel process. The Hearsay System also use the source of knowledge (acoustics-phonetics, prosody, lexicon, syntax, semantics and pragmatics) to generate hypothesis about what words might appear. The Hearsay System is incrementally trying to match words to the un-analyzed portion of the utterance
• If the resulting word matches are not optimal, the system backs up and tries a different path. The technique was known as probabilistic tree. The Hearsay System cannot recognize sentences in unrestricted English and can only recognize simple phrase structure languages.
• To be acceptable as an input medium, the system must not only perform with high accuracy, but it must also minimize the combinatorial explosion so as to respond as fast as a human would in a similar situation.

Dragon System ( Janice )

• Dragon
• Dragon
• Dragon

	Hearsay System	Dragon System
Model	cooperating parallel process	probabilistic function of Markov Process.
knowledge representation	Procedures.	Markov Networks.
Unit of Speech Signal Representation	segmentation	10 milli-second sample
search strategy	best-first	all paths in parallel
search time	8-50 times real-time	45-200 times real-time
What Have Been Tested	144 connected utterances 676 words 5 speakers 4 tasks: Chess, News Retrieval, Medical Diagnosis, Desk Calculator 28-76 word vocabularies	Janice, if you can't find relevant info you can put N/A
Performance	% of words spotted and identified correctly 89% with all the sources of knowledge 67% without semantics 44% without syntax and semantics (as of October, 1973)	Janice, if you can't find relevant info you can put N/A
Approaches	acoustics-phonetics, prosody, lexicon, syntax, semantics and pragmatics	Treats speech recognition as a mathematical computation problem rather than as an artificial intelligence problem.

Harpy Sytem

• detailed info of the current system
• with a table comparing three systems

Impact

• advantages (improvements on speech recognition tech

Key Innovations

• how the optimization make the improvements
• implications

Future research

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References

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