five

MODALITY corpus - SPEAKER 07 - COMMANDS C1

收藏
Mendeley Data2024-01-31 更新2024-06-28 收录
下载链接:
https://mostwiedzy.pl/en/open-research-data/modality-corpus-speaker-07-commands-c1,409012419419418-0
下载链接
链接失效反馈
官方服务:
资源简介:
The MODALITY corpus is one of the multimodal database of word recordings in English. It consists of over 30 hours of multimodal recordings. The database contains high-resolution, high-framerate stereoscopic video streams and audio signals obtained from a microphone array and a laptop microphone. The corpus can be employed to develop an AVSR system, as every utterance was labelled. Recordings in noisy conditions can be used to test the robustness of speech recognition systems. The language material was based on a remote control scenario and it includes 231 words -numbers, names of months and days, a set of verbs and nouns related to a computer device control. They were read by speakers as separated words and sequences resulting in a set of 12 recording sessions per speaker. Half of the sessions were recorded in quiet conditions, the other half contained three kinds of intrusive signals (traffic, babble and factory noise). The corpus includes recordings of 42 speakers (33 male, 9 female). The participants include 20 students and staff of Multimedia Systems Department of the Gdańsk University of Technology, 5 students of the Institute of English and American Studies of the University of Gdańsk, and 17 native English speakers. The dataset consist of recordings and visual features for SPEAKER 07: sex: man native speaker: no age: 28 The test material: COMMANDS C1 All recordings for all speakers are available at http://www.modality-corpus.org/ Sample still from the corpus(SPEAKER 07) Due to the size of the corpus (approx. 2.5 TB of data), every speaker’s recording was placed in a separate zip file of the size approx. 4-7 GB each. The recordings were organized according to the speakers’ language skills. The group A (17 speakers) consists of native-speakers. Non-native speakers recordings (Polish nationals) were placed in the Group B (25 speakers). The audio files use the Waveform Audio File Format (.wav), and contain a single PCM audio stream sampled at 44.1 kSa/s with 16-bit depth. The video files utilize the Matroska Multimedia Container Format (.mkv) in which a video stream in 1080p resolution, captured at 100 fps was placed after being compressed with h.265 codec (using High 4:4:4 profile). The ‘.lab’ files are text files containing the information on word positions in audio files, and follow the HTK label format. Each line of a ‘.lab’ file contains the actual label preceded by start and end times (in 100 ns units) e.g. : 1239620000 1244790000 FIVE which denotes the word “five”, occurring between the 123.962 s and 124.479 s of audio.Word-accurate SNR values calculated for every recording are also included in the ZIP file.

MODALITY语料库(MODALITY corpus)是英语单词录制类多模态数据库之一,总时长超过30小时。该数据库包含高分辨率、高帧率的立体视频流,以及由麦克风阵列和笔记本电脑麦克风采集的音频信号。由于所有语音片段均已标注,该语料库可用于开发音视频语音识别(Audio-Visual Speech Recognition, AVSR)系统;带噪环境下的录制数据则可用于测试语音识别系统的鲁棒性。 该语料的语言素材基于遥控器操控场景,涵盖231个单词,包括数字、月份与星期名称,以及一组与计算机设备操控相关的动词和名词。录制时由发音者以单个单词或单词序列的形式朗读,每位发音者共完成12组录制会话,其中一半会话在安静环境下录制,另一半则包含三种干扰信号(交通声、人声嘈杂声与工厂环境噪声)。 该语料库包含42位发音者的录制数据,其中33位为男性,9位为女性。参与者构成如下:格但斯克理工大学(Gdańsk University of Technology)多媒体系统系的20名学生与教职工、格但斯克大学(University of Gdańsk)英美研究学院的5名学生,以及17名以英语为母语的发音者。 本次公开的数据集包含编号为SPEAKER 07的发音者的录制数据与视觉特征:性别为男性,非英语母语者,年龄28岁。测试素材为命令类(COMMANDS C1)。所有发音者的全部录制数据均可通过 http://www.modality-corpus.org/ 获取,语料库(SPEAKER 07)样本帧截图可参考该站点。 由于该语料库总数据量约为2.5TB,每位发音者的录制数据均被打包为单独的ZIP压缩包,单包大小约为4-7GB。录制数据按照发音者的语言能力分为两组:A组(17位发音者)为以英语为母语的发音者;B组(25位发音者)为波兰籍的非英语母语发音者。 音频文件采用波形音频文件格式(Waveform Audio File Format, .wav),包含单路脉冲编码调制(PCM)音频流,采样率为44.1千次采样每秒(kSa/s),位深度为16比特。视频文件采用Matroska多媒体容器格式(.mkv),其中封装了采用H.265编解码器(High 4:4:4配置档)压缩的1080p分辨率、100帧率的视频流。 .lab文件为文本文件,包含音频文件中单词的位置信息,遵循HTK标注格式(HTK label format)。该文件的每一行均包含实际标注内容,其前为起始与结束时间戳(单位为100纳秒),示例如下:1239620000 1244790000 FIVE,该标注表示单词"five"出现在音频的123.962秒至124.479秒区间内。每个录制数据对应的逐词信噪比(SNR)计算值也包含在ZIP压缩包中。
创建时间:
2024-01-31
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作