Common Phone is a gender-balanced, multilingual corpus recorded from more than 76.000 contributors via Mozilla's Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation.
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ESB is a benchmark for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. It comprises eight English speech recognition datasets, capturing a broad range of domains, acoustic conditions, speaker styles, and transcription requirements.
This dataset includes all music sources, background noises and impulse-reponses (IR) samples and conversation speech that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" ICASSP 2021 (https://arxiv.org/abs/2010.11910).
The Flickr 8k Audio Caption Corpus contains 40,000 spoken captions of 8,000 natural images. It was collected in 2015 to investigate multimodal learning schemes for unsupervised speech pattern discovery. For a description of the corpus, see:
This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2].
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LaboroTVSpeech is a large-scale Japanese speech corpus built from broadcast TV recordings and their subtitles. It contains over 2,000 hours of speech.
MASRI-HEADSET is a corpus that was developed by the MASRI project at the University of Malta. It consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. The speakers were recruited from different geographical locations all over the Maltese islands, and were roughly evenly distributed by gender.
MultiSV is a corpus designed for training and evaluating text-independent multi-channel speaker verification systems. It can be readily used also for experiments with dereverberation, denoising, and speech enhancement.
The Norwegian Parliamentary Speech Corpus (NPSC) is a speech corpus made by the Norwegian Language Bank at the National Library of Norway in 2019-2021. The NPSC consists of recordings of speech from Stortinget, the Norwegian parliament, and corresponding orthographic transcriptions to Norwegian Bokmål and Norwegian Nynorsk. All transcriptions are done manually by trained linguists or philologists, and the manual transcriptions are subsequently proofread to ensure consistency and accuracy. Entire days of Parliamentary meetings are transcribed in the dataset.
NusaCrowd is a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, the authors have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments.
RyanSpeech is a speech corpus for research on automated text-to-speech (TTS) systems. This dataset contains textual materials from real-world conversational settings. These materials contain over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz.
VocBench is a framework that benchmark the performance of state-of-the art neural vocoders. VocBench uses a systematic study to evaluate different neural vocoders in a shared environment that enables a fair comparison between them.
We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dataset, which originally consists of 45 hours of audio and speech activity labels. To the best of our knowledge, the proposed AVASpeech-SMAD is the first open-source dataset that features strong polyphonic labels for both music and speech. The dataset was manually annotated and verified via an iterative cross-checking process. A simple automatic examination was also implemented to further improve the quality of the labels. Evaluation results from two state-of-the-art SMAD systems are also provided as a benchmark for future reference.
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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.
CLIPS, ovvero Corpora e Lessici dell'Italiano Parlato e Scritto, è uno degli otto progetti (Progetto n. 2) del Cluster C18 "LINGUISTICA COMPUTAZIONALE: RICERCHE MONOLINGUI E MULTILINGUI" (Legge 488), finanziato dal Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR).
CSI is a criminal conversational dataset for speaker identification built from the CSI television show. The authors collected transcripts of 39 episodes and video/audio of 4 episodes. Each episode involves on average more than 30 speakers. Utterances last on average 3 to 4 seconds. There are around 45 to 50 distinct scenes/conversations per episode.
CrowdSpeech is a publicly available large-scale dataset of crowdsourced audio transcriptions. It contains annotations for more than 20 hours of English speech from more than 1,000 crowd workers.
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This dataset is a new variant of the voice cloning toolkit (VCTK) dataset: device-recorded VCTK (DR-VCTK), where the high-quality speech signals recorded in a semi-anechoic chamber using professional audio devices are played back and re-recorded in office environments using relatively inexpensive consumer devices.
The EVI dataset is a challenging, multilingual spoken-dialogue dataset with 5,506 dialogues in English, Polish, and French. The dataset can be used to develop and benchmark conversational systems for user authentication tasks, i.e. speaker enrolment (E), speaker verification (V), speaker identification (I).
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The Edinburgh International Accents of English Corpus (EdAcc) is a new automatic speech recognition (ASR) dataset composed of 40 hours of English dyadic conversations between speakers with a diverse set of accents. EdAcc includes a wide range of first and second-language varieties of English and a linguistic background profile of each speaker.
EmoSpeech contains keywords with diverse emotions and background sounds, presented to explore new challenges in audio analysis.
FT Speech is a speech corpus created from the recorded meetings of the Danish Parliament, otherwise known as the Folketing (FT). The corpus contains over 1,800 hours of transcribed speech by a total of 434 speakers. It is significantly larger in duration, vocabulary, and amount of spontaneous speech than the existing public speech corpora for Danish, which are largely limited to read-aloud and dictation data.
Golos is a Russian speech dataset suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours.
We release the dataset for non-commercial research. Submit requests <a href="https://forms.gle/6WPEGNWbYoEe6bte8" target="_blank">here</a>.
Greek Parliament Proceedings is a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files.
The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report good performance on synthetic datasets where the train and test samples come from the same underlying distribution. However, the AEC performance often degrades significantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ) do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in realistic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 2,500 real audio devices and human speakers in real en
The INTERSPEECH 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report reasonable performance on synthetic datasets where the train and test samples come from the same underlying distribution. However, the AEC performance often degrades significantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ) do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in realistic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 5,000 real audio devices and human speakers
The Jejueo Single Speaker Speech (JSS) dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file.
JVS-MuSiC is a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer.
JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
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Kinect-WSJ is a multichannel, multispeaker, reverberated, noisy dataset which extends the WSJ0-2mix singlechannel, non-reverberated, noiseless dataset to the strong reverberation and noise conditions and the Kinect-like microphone array geometry used in CHiME-5.
The Kite database is a multi-modal dataset for the control of unmanned aerial vehicles (UAVs). There are three modalities present in the dataset:
LibriS2S is a Speech to Speech Translation (S2ST) dataset build further upon existing resources. The dataset provides English-German speech and text quadruplets ranging just over 50 hours for both languages.
MAVS is an audio-visual smartphone dataset captured in five different recent smartphones. This new dataset contains 103 subjects captured in three different sessions considering the different real-world scenarios. Three different languages are acquired in this dataset to include the problem of language dependency of the speaker recognition systems.
Here we release the dataset (Multi_Channel_Grid, abbreviated as MC_Grid) used in our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION.
We announce the release of a new multilingual speaker dataset called NITK-IISc Multilingual Multi-accent Speaker Profiling(NISP) dataset. The dataset contains speech in six different languages -- five Indian languages along with Indian English. The dataset contains speech data from 345 bilingual speakers in India. Each speaker has contributed about 4-5 minutes of data that includes recordings in both English and their mother tongue. The transcript for the text is provided in UTF-8 format. For every speaker, the dataset contains speaker meta-data such as L1, native place, medium of instruction, current residing place etc. In addition the dataset also contains physical parameter information of the speakers such as age, height, shoulder size and weight. We hope that the dataset is useful for a diverse set of research activities including multilingual speaker recognition, language and accent recognition, automatic speech recognition etc.
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.
Parkinson Speech Dataset is an audio dataset consisting of recordings of 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken. The goal is to classify which patients have Parkinson's.
Data collection was conducted by asking some adults from social media and some students from an elementary school to participate in our experiment. Table.1 shows the number of data gathered for recognizing each color. Due to the fact that two words are used for black in Persian, the number of black samples is more. In addition, because the color recognition is a RAN task, a sequence of data has been gathered. Table.2 depicts the number of sequence data for colors. For the meaningless words, 12 voices have been gathered on average for each word (there are 40 meaningless words in this task).
Quechua Collao corpus for automatic emotion recognition in speech. Audios are provided, alongside csv files with labels from 4 annotators for valence, arousal, and dominance values, using a 1 to 5 scale.
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RUSLAN is a Russian spoken language corpus for text-to-speech task. RUSLAN contains 22,200 audio samples with text annotations – more than 31 hours of high-quality speech of one person – being one of the largest annotated Russian corpus in terms of speech duration for a single speaker.
Situated Dialogue Navigation (SDN) is a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.
Facial electromyography recordings during both silent and vocalized speech.
The rise of singing voice synthesis presents critical challenges to artists and industry stakeholders over unauthorized voice usage. Unlike synthesized speech, synthesized singing voices are typically released in songs containing strong background music that may hide synthesis artifacts. Additionally, singing voices present different acoustic and linguistic characteristics from speech utterances. These unique properties make singing voice deepfake detection a relevant but significantly different problem from synthetic speech detection. In this work, we propose the singing voice deepfake detection task. We first present SingFake, the first curated in-the-wild dataset consisting of 28.93 hours of bonafide and 29.40 hours of deepfake song clips in five languages from 40 singers. We provide a train/val/test split where the test sets include various scenarios. We then use SingFake to evaluate four state-of-the-art speech countermeasure systems trained on speech utterances. We find these sys
Spot the Difference Corpus is a corpus of task-oriented spontaneous dialogues which contains 54 interactions between pairs of subjects interacting to find differences in two very similar scenes. The corpus includes rich transcriptions, annotations, audio and video.
Taiwanese Across Taiwan (TAT) corpus is a Large-Scale database of Native Taiwanese Article/Reading Speech collected across Taiwan. This corpus contains native Taiwanese speech of various accent across Taiwan. The corpus is annotated twice for use in voice recognition research. The corpus contains recording from 100 native speakers, each with length of 30 minutes making a total of 100 hours of speech data.
Dubbed series are gaining a lot of popularity in recent years with strong support from major media service providers. Such popularity is fueled by studies that showed that dubbed versions of TV shows are more popular than their subtitled equivalents.
The SWC is a corpus of aligned Spoken Wikipedia articles from the English, German, and Dutch Wikipedia. This corpus has several outstanding characteristics: