SRI International’s EduSpeak® system is a SDK that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology.
Effects of Vocal Effort and Speaking Style on Text-Independent Speaker Verification
We study the question of how intrinsic variations (associated with the speaker rather than the recording environment) affect text-independent speaker verification performance.
MUESLI: Multiple utterance error correction for a spoken language interface
We propose a method for using all available information to help correct recognition errors in tasks that use constrained grammars of the kind used in the domain of Command and Control (CC) systems.
Iterative Statistical Language Model Generation for Use with an Agent-Oriented Natural Language Interface
We describe a method for developing a statistical language model (SLM) with high keyword spotting accuracy for a natural language interface (NLI). The NLI is based on the Adaptive Agent Oriented Software Architecture (AAOSA).
The SRI EduSpeak(TM) System: Recognition and Pronunciation Scoring for Language Learning
The EduSpeak(TM) system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology.
The SRI March 2000 Hub-5 Conversational Speech Transcription System
We describe SRI’s large vocabulary conversational speech recognition system as used in the March 2000 NIST Hub-5E evaluation.
Collection and Detailed Transcription of a Speech Database for Development of Language Learning Technologies
We describe the methodologies for collecting and annotating a Latin-American Spanish speech database. We use the annotated database to investigate rater reliability, the effect of each phone on overall perceived nonnativeness, and the frequency of specific pronunciation errors.
A Study of Multilingual Speech Recognition
This paper describes our work in developing multilingual (Swedish and English) speech recognition systems in the ATIS domain. The acoustic component of the multilingual systems is realized through sharing Gaussian codebooks across Swedish and English allophones.
HMM State Clustering Across Allophone Class Boundaries
We present a novel approach to hidden Markov model (HMM) state clustering based on the use of broad phone classes and an allophone class entropy measure. Our algorithm allows clustering across allophone class boundaries by defining broad phone groups within which two states from different allophone classes can be clustered together.