Modeling answer seize rate (ASR) based on redial calls attempt

Seize the State , Seize the Day : State

Improving grapheme-based ASR by probabilistic lexical modeling approach

There is growing interest in using graphemes as subword units, especially in the context of the rapid development of hidden Markov model (HMM) based automatic speech recognition (ASR) system, as it eliminates the need to build a phoneme pronunciation lexicon. However, directly modeling the relationship between acoustic feature observations and grapheme states may not be always trivial. It usual...

Modeling auxiliary information in Bayesian network based ASR

Automatic speech recognition bases its models on the acoustic features derived from the speech signal. Some have investigated replacing or supplementing these features with information that can not be precisely measured (articulator positions, pitch, gender, etc.) automatically. Consequently, automatic estimations of the desired information would be generated. This data can degrade performance ...

Pronunciation modeling for ASR - knowledge-based and data-derived methods

This paper focuses on modeling pronunciation variation in two different ways: data-derived and knowledge-based. The knowledge-based approach consists of using phonological rules to generate variants. The data-derived approach consists of performing phone recognition, followed by smoothing using decision trees (D-trees) to alleviate some of the errors in the phone recognition. Using phonological...

Nonlinear dynamical system based acoustic modeling for ASR

The work presented here is centered around a speech production model called Chained Dynamical System Model (CDSM) which is motivated by the fundamental limitations of the mainstream ASR approaches. The CDSM is essentially a smoothly time varying continuous state nonlinear dynamical system, consisting of two sub dynamical systems coupled as a chain so that one system controls the parameters of t...