65,398 research outputs found
Simulating dysarthric speech for training data augmentation in clinical speech applications
Training machine learning algorithms for speech applications requires large,
labeled training data sets. This is problematic for clinical applications where
obtaining such data is prohibitively expensive because of privacy concerns or
lack of access. As a result, clinical speech applications are typically
developed using small data sets with only tens of speakers. In this paper, we
propose a method for simulating training data for clinical applications by
transforming healthy speech to dysarthric speech using adversarial training. We
evaluate the efficacy of our approach using both objective and subjective
criteria. We present the transformed samples to five experienced
speech-language pathologists (SLPs) and ask them to identify the samples as
healthy or dysarthric. The results reveal that the SLPs identify the
transformed speech as dysarthric 65% of the time. In a pilot classification
experiment, we show that by using the simulated speech samples to balance an
existing dataset, the classification accuracy improves by about 10% after data
augmentation.Comment: Will appear in Proc. of ICASSP 201
Searching Spontaneous Conversational Speech
The ACM SIGIR Workshop on Searching Spontaneous Conversational Speech was held as part of the 2007 ACM SIGIR Conference in Amsterdam.\ud
The workshop program was a mix of elements, including a keynote speech, paper presentations and panel discussions. This brief report describes the organization of this workshop and summarizes the discussions
Military applications of automatic speech recognition and future requirements
An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit
Focal Spot, Fall 1979
https://digitalcommons.wustl.edu/focal_spot_archives/1024/thumbnail.jp
An End-to-End Conversational Style Matching Agent
We present an end-to-end voice-based conversational agent that is able to
engage in naturalistic multi-turn dialogue and align with the interlocutor's
conversational style. The system uses a series of deep neural network
components for speech recognition, dialogue generation, prosodic analysis and
speech synthesis to generate language and prosodic expression with qualities
that match those of the user. We conducted a user study (N=30) in which
participants talked with the agent for 15 to 20 minutes, resulting in over 8
hours of natural interaction data. Users with high consideration conversational
styles reported the agent to be more trustworthy when it matched their
conversational style. Whereas, users with high involvement conversational
styles were indifferent. Finally, we provide design guidelines for multi-turn
dialogue interactions using conversational style adaptation
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