On the Use of Augmented Hmm Models for Overcoming Time and Parameter Independence Assumptions in Asr

Overcoming HMM Time and Parameter Independence Assumptions for ASR

How Neural Network Depth Compensates for HMM Conditional Independence Assumptions in DNN-HMM Acoustic Models

While DNN-HMM acoustic models have replaced GMMHMMs in the standard ASR pipeline due to performance improvements, one unrealistic assumption that remains in these models is the conditional independence assumption of the Hidden Markov Model (HMM). In this work, we explore the extent to which depth of neural networks helps compensate for these poor conditional independence assumptions. Using a bo...

End-to-End Speech Recognition Models

For the past few decades, the bane of Automatic Speech Recognition (ASR) systems have been phonemes and Hidden Markov Models (HMMs). HMMs assume conditional independence between observations, and the reliance on explicit phonetic representations requires expensive handcrafted pronunciation dictionaries. Learning is often via detached proxy problems, and there especially exists a disconnect betw...

What HMMs Can Do

Since their inception almost fifty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems — today, most state-of-the-art speech systems are HMM-based. There have been a number of ways to explain HMMs and to list their capabilities, each of these ways having both advantages and disadvantages. In an effort to better under...

A Study on the Use of Conditional Random Fields for Automatic Speech Recognition

Current state of the art systems for Automatic Speech Recognition (ASR) use statistical modeling techniques such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to recognize spoken language. These techniques make use of statistics derived from the acoustic frequencies of the speech signal. In recent years, interest has been rising in the use of phonological features derived fr...