The Automatic Speech recogition In Reverberant Environments (ASpIRE) challenge

By Mary Harper. 2015

98 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio, Signal Processing

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Citations in the corpus

CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “In parallel to research on acoustic robustness, research on conversational speech recognition has also made great progress, as illustrated by the recent announcements of super-human performance [22, 23] achieved on the Switchboard telephone conversation task [24] and by the ASpIRE challenge [25].”
Light Gated Recurrent Units for Speech Recognition (2018, methodology): “The development of robust ASR has been recently fostered by the great success of some international challenges such as CHiME [23], REVERB [24] and ASpIRE [25],”
Efficient Knowledge Distillation from an Ensemble of Teachers (2017, background): “Having demonstrated the impact of the proposed teacher-student schemes on this task, we explore the effectiveness of the same on two wellknow large vocabulary continuous speech recognition (LVCSR) tasks: Aspire [19] and Broadcast news.”
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, unknown): “In parallel to research on acoustic robustness, research on conversational speech recognition has also made great progress, as illustrated by the recent announcements of super-human performance [20, 21] achieved on the Switchboard telephone conversation task [22] and by the ASpIRE challenge [23].”
Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques (2019, background): “Notably, the Computational Hearing in Multisource Environments (CHiME) series of challenges [1], [2], the Reverberant Voice Enhancement and Recognition Benchmark (REVERB) Challenge [3], and the Automatic Speech Recognition in Reverberant Environments (ASpIRE) Challenge [4] were met with considerable”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, unknown): “, 2008), conversations (Harper, 2015), dialogues (Stupakov et al.”
A study on data augmentation of reverberant speech for robust speech recognition (2017, methodology): “To test the performance on far-field speech we use the ASpIRE dev set provided as part of the IARPA-ASpIRE challenge [3].”
Machine learning in acoustics: Theory and applications. (2019, background): “A number of datasets with labelled sound sources in a range of reverberant environments have been prepared (REVERB challenge; 250 ASpIRE challenge 251 ).”
Student-teacher network learning with enhanced features (2017, background): “Speech enhancement and ASR techniques have been developing rapidly with the growing demand for distant-talking speech interfaces and recent challenge activities [1, 2, 3, 4].”
Acoustic Modeling for Overlapping Speech Recognition: Jhu Chime-5 Challenge System (2019, background): “First, to tackle the distributed distant microphone setting, we explore data augmentation for improving robustness against a variety of room acoustics and directivity patterns of speech, which shows great success in noise robust ASR [7, 8, 9].”
Gpu-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition (2019, methodology): “We focus our examination on the performance of two models representing a wide spectrum of deployment conditions: from LibriSpeech [21] test-clean subset evaluated with a model tuned specifically for LibriSpeech, to the LibriSpeech testother subset evaluated on the ASPiRE [22] Kaldi model.”