In September of 1976, four speech understanding systems were demonstrated, signifying the end of a five‐year program of research and development sponsored by the Advanced Research Projects Agency (ARPA). The best performance was displayed by the Harpy system developed at Carnegie–Mellon University. Harpy satisfied a set of design goals that were specified at the beginning of the program, including the gal of understanding over 90% of a set of naturally spoken sentences composed from a 1000‐word lexicon. After defining the nature of the speech understanding problem, the four systems are described and critically evaluated. Based on this review, a structure for a next‐generation speech understanding system is proposed and parts of it are considered as a possible model of the early stages of speech perception. The perceptual model addresses the issue of lexical access and includes a decoding network composed of expected spectral sequences for all word strings of English.

Review of the ARPA speech understanding project

In this paper, we propose a direct method for speech rate estimation from acoustic features without requiring any automatic speech transcription. We compare various spectral and temporal signal analysis and smoothing strategies to better characterize the underlying syllable structure to derive speech rate. The proposed algorithm extends the methods of spectral sub- band correlation by including temporal correlation and the use of prominent spectral subbands for improving the signal correlation essential for syllable detection. Furthermore, to address some of the practical robustness issues in previously proposed methods, we introduce some novel components into the algorithm such as the use of pitch confidence for filtering spurious syllable envelope peaks, magnifying window for tackling neighboring syllable smearing, and relative peak measure thresholds for pseudo peak rejection. We also describe an automated approach for learning algorithm parameters from data, and find the optimal settings through Monte Carlo simulations and parameter sensitivity analysis. Final experimental evaluations are conducted based on a portion of the Switchboard corpus for which manual phonetic segmentation information, and published results for direct comparison are available. The results show a correlation coefficient of 0.745 with respect to the ground truth based on manual segmentation. This result is about a 17% improvement compared to the current best single estimator and a 11% improvement over the multiestimator evaluated on the same Switchboard database.

/pdf/robust-speech-rate-estimation-for-spontaneous-speech-4mnjfk13tq.pdf

Robust Speech Rate Estimation for Spontaneous Speech

The paper continues the authors' investigation of machines that adaptively acquire language through interaction with a complex environment. In particular, the present work focuses on the problem of spoken word acquisition, using the authors' proposed principles to motivate a method to govern the emergence of word symbols from the speech signal. The mechanism involves a connectionist network embedded in a feedback control system. The resulting system has two unique characteristics. First, no text is utilized by the device, in contrast to all other speech understanding systems. Second, the vocabulary and grammar is unconstrained, being acquired by the device during the course of performing its task. This is also in contrast to all other systems, in which the salient vocabulary and grammar are preprogrammed. A rudimentary baseline experiment is described, involving 1105 natural language utterances in an automated call routing application scenario. >

An experiment in spoken language acquisition

: This dissertation contributes in three main areas: (1) theory of multimodal interaction, (2) software architecture and reusable application framework, and (3) rapid application prototyping by domain specific instantiation of a common underlying architecture. The foundation of the application framework and the rapid prototyping tools is a model of multimodal interpretation based on semantic integration of information streams. This model supports most of the conceivable human communication modalities in the context of a broad class of applications, specifically those that support state manipulation via parameterized actions. The multimodal semantic model is also the basis for a flexible, domain independent, incrementally trainable multimodal interpretation algorithm based on a connectionist network. The second major contribution is an application framework consisting of reusable components and a modular, distributed system architecture. Multimodal application developers can assemble the components in the framework into a new application, accepting default options when appropriate and providing application specific customizations when needed. The third major contribution is a design process backed by a workbench of tools to permit the rapid prototyping of a multimodal application. This design process systematically constructs customizations needed to interpret multimodal inputs in a given domain, allowing an application structure created in the proposed framework to be instantiated for that domain. The application framework and design process have been successfully applied to the construction of three multimodal systems in three different domains.

A framework and toolkit for the construction of multimodal learning interfaces

Sperry Univac is developing a linguistically oriented system for locating important words in conversational speech. The system uses acoustic, prosodic, and phonetic analyses to produce a phonetic description of the incoming speech. Next, phonetic dictionary representations of the keywords to be found are compared to all portions of the phonetic analysis. High scoring matches are then verified by aligning prestored spectral patterns with the spectral information found during analysis, and resulting good matches are announced as likely keyword occurrences. Current results are presented for this system, which is being developed and tested on bandlimited, conversational speech from a large, diverse speaker population.

An automatic word spotting system for conversational speech

Sperry Univac is developing a linguistically oriented computer system which recognizes natural spoken phrases and sentences without requiring extensive adjustments for individual users and without the need for each user to repeat every vocabulary word for system training. The recognition system produces a linguistic description of the unknown utterance by determining its component sound segments. Words from a dictionary, or lexicon, (represented in terms of the same linguistic segments) are then strung together and compared to the analysis segments to determine which syntactically allowed sequence of vocabulary words best matches the unknown utterance. Two data bases have been used recently for development and testing, including alphanumeric sequences constructed from a vocabulary of 36 words, and data management commands from a vocabulary of 64 words. For both data bases, average correct recognition scores were 95% for individual words, and 88% for complete sentences. Current plans for enhancing the recognition system include enlarging the vocabulary and number of recognizable sentence types, making additional use of prosodic information, and introducing phonetic verification rules and a word verification component. The ultimate goal of this development is to provide an effective sentence recognition system for natural and practical speech input to computers.

http://ieeexplore.ieee.org/iel6/8365/26347/01170372.pdf

A system for the recognition of spoken connected word sequences

Sperry Univac is developing a linguistically oriented system for locating key information-carrying words in conversational speech. The system first analyzes the speech acoustically, providing information on bandlimited energies and fundamental and formant frequencies. A prosodic analysis then indicates the location of syllables. Finally, a phonetic word verification technique is exercised on each syllable. Prestored acoustic properties characteristic of each phonetic segment of each keyword are compared against the incoming acoustic properties found at appropriate locations anchored within and around the syllables. Acoustic-phonetic parameter alignments exhibiting great similarity are announced as likely keyword occurrences. Current results are presented for this system, which is being developed and tested on noisy, bandlimited, conversational speech from a large and diverse speaker population.

Word spotting in conversational speech

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A system for the recognition of spoken connected word sequences

An automatic word spotting system for conversational speech

Word spotting in conversational speech