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Skip to Search Results- 4speech recognition
- 2audio segmentation
- 2forced alignment
- 1acoustics
- 1cognitive science
- 1deep learning
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2018-01-01
Matthew C. Kelley, Benjamin V. Tucker
Poster for the paper "A comparison of input types to a deep neural network-based forced aligner," presented at Interspeech 2018. Typo in alignment matrix (O[2,2] referenced O[1,2] instead of O[1,1]) updated on June 4, 2019. PAPER ABSTRACT: The present paper investigates the effect of different...
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2018-01-01
Matthew C. Kelley, Benjamin V. Tucker
The present paper investigates the effect of different inputs on the accuracy of a forced alignment tool built using deep neural networks. Both raw audio samples and Mel-frequency cepstral coefficients were compared as network inputs. A set of experiments were performed using the TIMIT speech...
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2021-04-16
Call Centers or Support Centers in different companies aggregate huge amount of audio data everyday. From all the conversations, few conversations demonstrate the disappointment of clients towards services, products or delivery. Finding the sentiment of the customer helps in determining whether...
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2022-01-01
Matthew C. Kelley, Benjamin V. Tucker
Using phonological neighborhood density has been a common method to quantify lexical competition. It is useful and convenient but has shortcomings that are worth reconsidering. The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance...