This article presents an experiment with seniors and people with visual impairment in a voice-controlled smart home using the SWEET-HOME system. The experiment shows some weaknesses in automatic speech recognition that must be addressed, as well as the need for better adaptation to the user and the environment. Users were disturbed by the rigid structure of the grammar and were eager to adapt it to their own preferences. Surprisingly, while no humanoid aspect was introduced in the system, the senior participants were inclined to embody the system. Despite these aspects to improve, the system has been favorably assessed as diminishing most participant fears related to the loss of autonomy. . 2015. Evaluation of a context-aware voice interface for Ambient Assisted Living: qualitative user study vs. quantitative system evaluation.
Abstract-The SWEET-HOME project aims at providing audio-based interaction technology that lets the user have full control over her home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the multimodal sound corpus acquisition and labelling and on the investigated techniques for speech and sound recognition. The user study and the recognition performances show the interest of this audio technology.
In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably predicts the WER distribution on a collection of speech recordings.
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA
This study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system.
In voice controlled multi-room smart homes ASR and speaker identification systems face distance speech conditions which have a significant impact on performance. Regarding voice command recognition, this paper presents an approach which selects dynamically the best channel and adapts models to the environmental conditions. The method has been tested on data recorded with 11 elderly and visually impaired participants in a real smart home. The voice command recognition error rate was 3.2% in off-line condition and of 13.2% in online condition. For speaker identification, the performances were below very speaker dependant. However, we show a high correlation between performance and training size. The main difficulty was the too short utterance duration in comparison to state of the art studies. Moreover, speaker identification performance depends on the size of the adapting corpus and then users must record enough data before using the system.
This paper describes our Word-level QE system for WMT 2014 shared task on Spanish -English pair. Compared to WMT 2013, this year's task is different due to the lack of SMT setting information and additional resources.We report how we overcome this challenge to retain most of the important features which performed well last year in our system. Novel features related to the availability of multiple systems output (new point of this year) are also proposed and experimented along with baseline set. The system is optimized by several ways: tuning the classification threshold, combining with WMT 2013 data, and refining using Feature Selection strategy on our development set, before dealing with the test set for submission.
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