In this case study, we use conversational data from the Switchboard corpus to investigate the functional opposition between two pragmatically specialized constructions of English: Topicalization and Left Dislocation. Specifically, we use distributional trends in the Switchboard corpus to revise several conclusions reached by Prince (1981a, 1981b, 1997) concerning the function of Left Dislocatio...

In this paper, we explore the effectiveness of a variety of Deep Learning-based acoustic models for conversational telephony speech, specifically TDNN, bLSTM and CNN-bLSTM models. We evaluated these models on both research testsets, such as Switchboard and CallHome, as well as recordings from a realworld call-center application. Our best single system, consisting of a single CNN-bLSTM acoustic ...

The detection of prosodic characteristics is an important aspect of both speech synthesis and speech recognition. Correct placement of pitch accents aids in more natural sounding speech, while automatic detection of accents can contribute to better wordlevel recognition and better textual understanding. In this paper we investigate probabilistic, contextual, and phonological factors that influe...

Automatic detection of vowel landmarks is useful in many applications such as automatic speech recognition (ASR), audio search, syllabification of speech and expressive speech processing. In this paper, acoustic features extracted around epochs are proposed for detection of vowel landmarks in continuous speech. These features are based on zero frequency filtering (ZFF) and single frequency filt...

In conventional language modeling, the words from only one speaker are represented at a time, even for conversational tasks such as meetings and telephone calls. In a conversational or meeting setting, however, different speakers can influence each other. In order to recover this missing inter-speaker information, in this work we present a novel approach for conversational language modeling tha...

This paper reports our on-going work aimimg to improve the performance of a novel speech recognizer based on an underlying statistical hidden dynamic model of phonetic reduction in the production of conversational speech. We have developed a path-stack search algorithm which e ciently computes the likelihood of any observation utterance while optimizing the dynamic regimes in the speech model. ...

In this paper we report our recent research whose goal is to improve the performance of a novel speech recognizer based on an underlying statistical hidden dynamic model of phonetic reduction in the production of conversational speech. We have developed a path-stack search algorithm which efficiently computes the likelihood of any observation utterance while optimizing the dynamic regimes in th...

Conversational speech recognition has served as a flagship speech recognition task since the release of the DARPA Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcriptionists is 5.9% for the Switchboard portion of the data...

In this paper we present a set of techniques we employed in our Janus Recognition Toolkit (JRTk) Switchboard and CallHome recognizer in order to deal with imperfections in the transcriptions: inconsistent transcription of pronunciations and contractions as well as errors in utterance segmentations. These techniques consist of a dynamic, speaking mode dependent pronunciation model and a exible u...