R. Pieraccini

Experimental comparison among data compression techniques in isolated word recognition

It is well known that the isolated word recognition strategy based on pattern matching gives good performance; however, in order to achieve efficient implementation it is necessary to develop techniques to reduce computational complexity and memory requirements, especially when the vocabulary size is not very small. In this work three different pattern compression techniques are compared on the basis of efficiency as well as recognition performance when applied to pattern matching by means of dynamic programming in a speaker dependent context.

Experimental results on large-vocabulary continuous speech recognition and understanding

A continuous speech recognition and understanding system is presented that accepts queries about a restricted geographical domain, expressed in free but syntactically correct natural language, with a lexicon of the order of one thousand words. A lattice of word candidates hypothesized by the speaker dependent recognition level is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition subsystem generates word hypotheses by exploiting hidden Markov models of sub-word units. Bottom-up constraints are also introduced to restrict the set of candidate words. The understanding module determines the most likely sequence of words and represents its meaning in a parse-tree suitable to access a database. It makes use of a modified caseframe analysis driven by the word hypotheses likelihood scores. The results of a set of experiments performed in 150 sentences collected from one speaker are given.<<ETX>>

Interaction between fast lexical access and word verification in large vocabulary continuous speech recognition

Recently a two step strategy for large vocabulary isolated word recognition has been successfully experimented. The first step consists in the hypothesization of a reduced set of word candidates on the basis of broad bottom-up features, while the second one is the verification of the hypotheses using more detailed phonetic knowledge. This paper deals with its extension to continuous speech. A tight integration between the two steps rather than a hierarchical approach has been investigated. The hypothesization and the verification modules are implemented as processes running in parallel. Both processes represent lexical knowledge by a tree. Each node of the hypothesization tree is labeled by one of 6 broad phonetic classes. The nodes of the verification tree are, instead, the states of sub-word HMMs. The two processes cooperate to detect word hypotheses along the sentence.<<ETX>>

A word hypothesizer for a large vocabulary continuous speech understanding system

A continuous-speech recognition and understanding system for a thousand-word vocabulary has been designed and implemented. It is able to answer queries put to a geographical database in natural Italian language. A discussion is presented of the recognition component of the system. It can produce a word lattice that is then processed by a syntactic-semantic component. In addition, a linguistic decoder exploiting word-pair constraints has been investigated. Its results have been compared to those obtained by similar approaches reported in the literature. The system relies on word preselection through lexical access by means of broad phonetic classes and on hidden Markov modeling of subword units. The improvements to the basic approach are presented and system performance is given. Average word accuracy and correct sentence recognition obtained for speaker-dependent tests performed by two speakers pronouncing 214 sentences are 94.5% and 89.3%, respectively. The perplexity of the word-pair language model is 25.<<ETX>>

Strategies for lexical access to very large vocabularies

Abstract A large vocabulary isolated word recognition system is described on a two pass strategy: word hypothesization and verification. Word preselection is achieved by segmenting and classifying the input signal in terms of 6 broad phonetic classes. To reduce storage and computational costs, lexical knowledge is organized in a tree structure where the initial common subsequences of word descriptions are shared, and a beam-search Dynamic Programming algorithm carries on the most promising paths only. In the second pass, word verification, a detailed representation of the phonemic structure of word candidates is used for estimating the most likely words. Each word candidate is modeled by a graph of subword Hidden Markov Models. Again, a tree-structure of the whole word subset is built online for an efficient implementation of a beam-search Viterbi algorithm that estimates the likelihood of the candidates. The results show that a complexity reduction of about 73% can be achieved by using the two pass approach with respect to the direct approach, while the recognition accuracy remains comparable.

Experimental results on a large lexicon access task

In this paper the problem of lexical access to large vocabularies by means of a coarse phonetic description of words is addressed. A generate and test approach is used: first a set of word candidates is extracted from the lexicon by means of a broad phonetic description of the input utterance, then a more detailed stochastic model of each word in this set, based on sub-word phonetic units, is obtained, and the likelihood of the candidate words is estimated using the Viterbi algorithm. Results of the application of the method to a large vocabulary isolated word recognition task are given. The candidate lists produced in the generation phase include the correct word in 98 times out of 100, their average size is of the order of 50 items for a 1011 word lexicon, while they do not exceed 300 units for a 13748 word lexicon.

Speaker independent recognition of Italian telephone speech with mixture density hidden Markov models

Abstract The results of a recognition experiment, conducted on a speaker independent continuous Italian speech database, are reported in this paper. The recognition system is based on mixture density hidden Markov models of phonetic units. Different sets of units were tested, beginning with the most general context independent phones and ending with the most specific triphones; function word dependent units were also investigated. The recognition, based on a vocabulary of 979 words, was performed with no linguistic constraints, i.e., with a word branching factor of 979. The results confirm the effectiveness of context dependent units, for which a word accuracy of nearly 80% was obtained on a set of 300 sentences.

The Recognition Algorithms

Subtask 2.1 of the P26 project was devoted to the study of the problems related to the development of the front-end of a speech understanding system. In the early stages of the project it was decided to separate the front-end, referred to in the following as the recognition module, from the understanding module, that deals with syntax and semantics. This decision was drawn taking into account several considerations mainly based on a practical point of view: the research groups working on Subtask 2.1 were at their first experience with speech understanding systems and their background was mainly in developing systems for small-vocabulary isolated and connected word recognition. Approaching the speech understanding problem required a strong effort both in knowledge acquisition and software development. For instance, methodologies for dealing with phonetic transcriptions of lexical items had to be developed from the beginning. More important was the lack of any practical feeling about the problem. Nobody knew (and very few in the world did at that time) what performance could be realistically achieved using a 1000-word vocabulary with a system based on sub-word unit modeling, hence which integrated strategy should be planned to attain a reasonably good understanding of the spoken sentences. The choice of a two-module system with a one-way interaction seemed the most appropriate for starting to acquire the proper knowledge on the problem. Besides, as people working on the two modules belonged to different groups and used different techniques as well as different programming languages (stochastic modeling and FORTRAN for the reeognition group, knowledge-based parsing and LISP for the understanding group), the best solution looked like the one by which the development of the two modules did not have to suffer from unavoidable mutual time dependencies.