Sciweavers

50 search results - page 5 / 10
» Speaker and Channel Factors in Text-Dependent Speaker Recogn...
Sort
View
ICASSP
2011
IEEE
12 years 11 months ago
Cross-Channel Spectral Subtraction for meeting speech recognition
We propose Cross-Channel Spectral Subtraction (CCSS), a source separation method for recognizing meeting speech where one microphone is prepared for each speaker. The method quick...
Yu Nasu, Koichi Shinoda, Sadaoki Furui
PERCOM
2010
ACM
13 years 9 months ago
Collaborative real-time speaker identification for wearable systems
We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already k...
Mirco Rossi, Oliver Amft, Martin Kusserow, Gerhard...
ICASSP
2009
IEEE
14 years 2 months ago
Support vector machines and Joint Factor Analysis for speaker verification
This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the...
Najim Dehak, Patrick Kenny, Réda Dehak, Ond...
ICASSP
2011
IEEE
12 years 11 months ago
User verification: Matching the uploaders of videos across accounts
This article presents an attempt to link the uploaders of videos based on the audio track of the videos. Using a subset of the MediaEval [10] Placing Task’s Flickr video set, wh...
Howard Lei, Jaeyoung Choi, Adam Janin, Gerald Frie...
SPEECH
2010
89views more  SPEECH 2010»
13 years 2 months ago
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates
Despite years of speech recognition research, little is known about which words tend to be misrecognized and why. Previous work has shown that errors increase for infrequent words...
Sharon Goldwater, Daniel Jurafsky, Christopher D. ...