Keyword Spotting

Speech Commands is an audio dataset of spoken words designed to help train and evaluate keyword spotting systems .

The Lip Reading in the Wild (LRW) dataset a large-scale audio-visual database that contains 500 different words from over 1,000 speakers. Each utterance has 29 frames, whose boundary is centered around the target word. The database is divided into training, validation and test sets. The training set contains at least 800 utterances for each class while the validation and test sets contain 50 utterances. Source: Towards Pose-invariant Lip-Reading Image Source: https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrw1.html

The Oxford-BBC Lip Reading Sentences 2 (LRS2) dataset is one of the largest publicly available datasets for lip reading sentences in-the-wild. The database consists of mainly news and talk shows from BBC programs. Each sentence is up to 100 characters in length. The training, validation and test sets are divided according to broadcast date. It is a challenging set since it contains thousands of speakers without speaker labels and large variation in head pose. The pre-training set contains 96,318 utterances, the training set contains 45,839 utterances, the validation set contains 1,082 utterances and the test set contains 1,242 utterances. Source: Audio-visual Recognition of Overlapped speech for the LRS2 dataset Image Source: https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html

LRS3-TED is a multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research. Source: LRS3-TED: a large-scale dataset for visual speech recognition

TAU Urban Acoustic Scenes 2019 development dataset consists of 10-seconds audio segments from 10 acoustic scenes: airport, indoor shopping mall, metro station, pedestrian street, public square, street with medium level of traffic, travelling by a tram, travelling by a bus, travelling by an underground metro and urban park. Each acoustic scene has 1440 segments (240 minutes of audio). The dataset contains in total 40 hours of audio. Source: https://zenodo.org/record/2589280 Image Source: http://dcase.community/challenge2019/task-acoustic-scene-classification#citation

VoxForge is an open speech dataset that was set up to collect transcribed speech for use with Free and Open Source Speech Recognition Engines (on Linux, Windows and Mac). Image Source: http://www.voxforge.org/home

The PodcastFillers dataset consists of 199 full-length podcast episodes in English with manually annotated filler words and automatically generated transcripts. The podcast audio recordings, sourced from SoundCloud, are CC-licensed, gender-balanced, and total 145 hours of audio from over 350 speakers. The annotations are provided under a non-commercial license and consist of 85,803 manually annotated audio events including approximately 35,000 filler words (“uh” and “um”) and 50,000 non-filler events such as breaths, music, laughter, repeated words, and noise. The annotated events are also provided as pre-processed 1-second audio clips. The dataset also includes automatically generated speech transcripts from a speech-to-text system. A detailed description is provided in Dataset.

This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2]. It could be downloaded here: https://zenodo.org/record/6066174#.Yn7NPJPMLyU Specifically, we created this test set by mixing the speech in the Google Speech Commands v2 test set with random noise in the Musan dataset at different signal to noise ratio -12.5,-10,0,10,20,30 and 40 decibel (dB). The Google Speech Commands v2 dataset is under the Creative Commons BY 4.0 license. It could be downloaded at: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz The Musan dataset is under Attribution 4.0 International (CC BY 4.0). It could be downlowned at https://www.openslr.org/17/ Citations: [1] Pete Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” arXiv preprint arXiv:1804.03209, 2018. [2] David Snyder, Guoguo Chen, and Daniel Povey, “Musan: A music, speech, and noise corpus,” arXiv preprint arXiv:1510.08484, 2015.

The football keyword dataset (FKD), as a new keyword spotting dataset in Persian, is collected with crowdsourcing. This dataset contains nearly 31000 samples in 18 classes.

This dataset is designed to help train simple machine learning models that serve educational and research purposes in the speech recognition domain, mainly for keyword spotting tasks. ## Dataset Description Our dataset is a list of pairs (x, y), where x is the input speech signal, and y is the corresponding keyword. The final dataset consists of 12000 such pairs, comprising 40 keywords. Each audio file is one-second in length sampled at 16 kHz. We have 30 participants, each of them recorded 10 utterances for each keyword. Therefore, we have 300 audio files for each keyword in total (30 * 10 * 40 = 12000), and the total size of all the recorded keywords is ~384 MB. The dataset also contains several background noise recordings we obtained from various natural sources of noise. We saved these audio files in a separate folder with the name background_noise and a total size of ~49 MB. ## Dataset Structure There are 40 folders, each of which represents one keyword and contains 300 files. The first eight digits of each file name identify the contributor, while the last two digits identify the round number. For example, the file path rotate/00000021_NO_06.wav indicates that the contributor with the ID 00000021 pronounced the keyword rotate for the 6th time. ## Data Split We recommend using the provided CSV files in your experiments. We kept 60% of the dataset for training, 20% for validation, and the remaining 20% for testing. In our split method, we guarantee that all recordings of a certain contributor are within the same subset. ## License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. For more details, see the LICENSE file in this folder. ## Citations If you want to use the Arabic Speech Commands dataset in your work, please cite it as: @article{ghandoura2021building, title={Building and benchmarking an Arabic Speech Commands dataset for small-footprint keyword spotting}, author={Ghandoura, Abdulkader and Hjabo, Farouk and Al Dakkak, Oumayma}, journal={Engineering Applications of Artificial Intelligence}, volume={102}, pages={104267}, year={2021}, publisher={Elsevier} }

Auto-KWS is a dataset for customized keyword spotting, the task of detecting spoken keywords. The dataset closely resembles real world scenarios, as each recorder is assigned with an unique wake-up word and can choose their recording environment and familiar dialect freely. All data is recorded by near-field mobile phones, (located in front of the speakers at around 0.2m distance). Each sample is recorded in single channel, 16-bit streams at a 16kHz sampling rate. There are 4 datasets: training dataset, practice dataset, feedback dataset, and private dataset. Training dataset, recorded from around 100 recorders, is used for participants to develop Auto-KWS solutions. Practice dataset contains 5 speakers data, each with 5 enrollment audio data and seveal test audio. Practice dataset together with the downloadable docker provides an example of how platform would call the participants' code. Both Training and practice dataset can be downloaded for local debugging. The feedback dataset and private dataset have the same format of practice dataset and are used for final evaluation and thus will be hiden from participants.

EmoSpeech contains keywords with diverse emotions and background sounds, presented to explore new challenges in audio analysis.