6 papers
End-to-End Speech Recognition and Disfluency Removal
Paria Jamshid Lou, Mark Johnson
Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the tas…
Improving Disfluency Detection by Self-Training a Self-Attentive Model
Paria Jamshid Lou, Mark Johnson
Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detectio…
ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection
Omid Mohamad Nezami, Paria Jamshid Lou, Mansoureh Karami
This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent…
Neural Constituency Parsing of Speech Transcripts
Paria Jamshid Lou, Yufei Wang, Mark Johnson
This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack…
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
Paria Jamshid Lou, Mark Johnson
This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-bes…
Disfluency Detection using Auto-Correlational Neural Networks
Paria Jamshid Lou, Peter Anderson, Mark Johnson
In recent years, the natural language processing community has moved away from task-specific feature engineering, i.e., researchers discovering ad-hoc feature representations for v…