The REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge is a benchmark for evaluation of automatic speech recognition techniques. The challenge assumes the scenario of capturing utterances spoken by a single stationary distant-talking speaker with 1-channe, 2-channel or 8-channel microphone-arrays in reverberant meeting rooms. It features both real recordings and simulated data.
49 PAPERS • 1 BENCHMARK
WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the speech sources in addition to the existing noise. Room impulse responses were generated and convolved using pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and further classified as high, medium, and low reverberation based on a qualitative assessment of the mixture’s noise recording.
45 PAPERS • 3 BENCHMARKS
VoiceBank+DEMAND is a noisy speech database for training speech enhancement algorithms and TTS models. The database was designed to train and test speech enhancement methods that operate at 48kHz. A more detailed description can be found in the paper associated with the database. Some of the noises were obtained from the Demand database, available here: http://parole.loria.fr/DEMAND/ . The speech database was obtained from the Voice Banking Corpus, available here: http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz .
33 PAPERS • 1 BENCHMARK
The Easy Communications (EasyCom) dataset is a world-first dataset designed to help mitigate the cocktail party effect from an augmented-reality (AR) -motivated multi-sensor egocentric world view. The dataset contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head and face bounding boxes and source identification labels. We have created and are releasing this dataset to facilitate research in multi-modal AR solutions to the cocktail party problem.
15 PAPERS • 4 BENCHMARKS
L3DAS22: MACHINE LEARNING FOR 3D AUDIO SIGNAL PROCESSING This dataset supports the L3DAS22 IEEE ICASSP Gand Challenge. The challenge is supported by a Python API that facilitates the dataset download and preprocessing, the training and evaluation of the baseline models and the results submission.
12 PAPERS • NO BENCHMARKS YET
L3DAS21 is a dataset for 3D audio signal processing. It consists of a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage.
5 PAPERS • 2 BENCHMARKS
A Brazilian Portuguese TTS dataset featuring a female voice recorded with high quality in a controlled environment, with neutral emotion and more than 20 hours of recordings. with neutral emotion and more than 20 hours of recordings. Our dataset aims to facilitate transfer learning for researchers and developers working on TTS applications: a highly professional neutral female voice can serve as a good warm-up stage for learning language-specific structures, pronunciation and other non-individual characteristics of speech, leaving to further training procedures only to learn the specific adaptations needed (e.g. timbre, emotion and prosody). This can surely help enabling the accommodation of a more diverse range of female voices in Brazilian Portuguese. By doing so, we also hope to contribute to the development of accessible and high-quality TTS systems for several use cases such as virtual assistants, audiobooks, language learning tools and accessibility solutions.
1 PAPER • NO BENCHMARKS YET
A database containing high sampling rate recordings of a single speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for speech synthesis and automatic speech recognition applications, the dataset contains text extracted from a popular Brazilian news TV program, totalling roughly 20 h of audio spoken by a trained individual in a controlled environment. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples.
The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. Each file is labelled with subjective ratings of the overall quality and the quality dimensions Noisiness, Coloration, Discontinuity, and Loudness. In total, it contains more than 97,000 human ratings for each of the dimensions and the overall MOS.
WHAMR_ext is an extension to the WHAMR corpus with larger RT60 values (between 1s and 3s)
1 PAPER • 1 BENCHMARK
We present a multilingual test set for conducting speech intelligibility tests in the form of diagnostic rhyme tests. The materials currently contain audio recordings in 5 languages and further extensions are in progress. For Mandarin Chinese, we provide recordings for a consonant contrast test as well as a tonal contrast test. Further information on the audio data, test procedure and software to set up a full survey which can be deployed on crowdsourcing platforms is provided in our paper [arXiv preprint] and GitHub repository. We welcome contributions to this open-source project.