Introduction

Welcome to the Mandarin Chinese General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Mandarin speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mandarin Chinese communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Mandarin speech models that understand and respond to authentic Chinese accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mandarin Chinese. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Mandarin speech and language AI applications:

Languages:Percent Chinese Speakers: Basic demographics by census tracts in King County based on current American Community Survey 5 Year Average (ACS). Included demographics are: total population; foreign born; median household income; English language proficiency; languages spoken; race and ethnicity; sex; and age. Numbers and derived percentages are estimates based on the current year's ACS. GEO_ID_TRT is the key field and may be used to join to other demographic Census data tables.

This database contains the recordings of 500 Chinese Mandarin speakers from Northern China (250 males and 250 females), from 18 to 60 years’ old, recorded in quiet studios located in Shenzhen and in Hong Kong Special Administrative Region, People’s Republic of China. Demographics of native speakers from Northern China is as follows:- Beijing: 200 speakers (100 males, 100 females)- North of Beijing: 101 speakers (50 males, 51 females)- Shandong: 149 speakers (75 males, 74 females)- Henan: 50 speakers (25 males, 25 females)Speaker profile includes the following information: unique ID, place of birth, place where speaker lived the longest by the age of 16, and the number of years that the speaker lived there, age, gender, recording place.Recordings were made through microphone headsets (ATM73a / AUDIO TECHNICA) and consist of 172 hours of audio data (about 30 minutes per speaker), stored in .WAV files as sequences of 48 KHz Mono, 16 bits, Linear PCM. Recording script consists of :• Phoneme balance statement: 785 sentences• Travel conversation: 1618 sentences• About 200 sentences per speaker including: 134 sentences of travel conversation, 66 sentences of phoneme balance

Mandarin Chinese Spontaneous Dialogue Paralanguage Annotated Speech Synthesis Corpus, recorded by 370 Chinese native speakers, natural conversation style. Professional phonetician annotationed 14 kinds of paralanguages, transcriptions, speakers, and so on, precisely matches with the research and development needs of the speech synthesis.

This database contains the recordings of 1000 Chinese Mandarin speakers from Southern China (500 males and 500 females), from 18 to 60 years’ old, recorded in quiet studios located in Shenzhen and in Hong Kong Special Administrative Region, People’s Republic of China. Demographics of native speakers from Southern China is as follows:- Guangdong: 312 speakers (154 males, 158 females)- Fujian: 155 speakers (95 males, 60 females)- Jiangsu: 262 speakers (134 males, 128 females)- Zhejiang: 160 speakers (84 males, 76 females)- Taiwan: 105 speakers (31 males, 74 females)- Other-Southern: 6 speakers (2 males, 4 females)Speaker profile includes the following information: unique ID, place of birth, place where speaker lived the longest by the age of 16, and the number of years that the speaker lived there, age, gender, recording place.Recordings were made through microphone headsets (ATM73a / AUDIO TECHNICA) and consist of 341 hours of audio data (about 30 minutes per speaker), stored in .WAV files as sequences of 48 KHz Mono, 16 bits, Linear PCM. Recording script consists of :• Phoneme balance statement: 785 sentences• Travel conversation: 1618 sentences• About 200 sentences per speaker including: 134 sentences of travel conversation, 66 sentences of phoneme balance

Taiwanese Mandarin(China) Scripted Monologue Smartphone speech dataset, collected from monologue based on given prompts, covering economy, entertainment, news, oral language, numbers, alphabet and other domains. Transcribed with text content. Our dataset was collected from extensive and diversify speakers(204 native speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Mandarin speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Mandarin Chinese speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

This dataset was recorded within the Deutsche Forschungsgemeinschaft (DFG) project: Experiments and models of speech recognition across tonal and non-tonal language systems (EMSATON, Projektnummer 415895050).

The Lombard effect or Lombard reflex is the involuntary tendency of speakers to increase their vocal effort when speaking in loud noise to enhance the audibility of their voice. Up to date, the phenomena of Lombard effects were observed in different languages. The present database aimed at providing recordings for studying the Lombard effect with Mandarin speech.

Eleven native-Mandarin talkers (6 female and 5 male) were recruited, both Lombard/plain speech were recorded from the same talker in the same day.

All speakers produced fluent standard Mandarin speech (North China). All listeners were normal-hearing with pure tone thresholds of 20 dB hearing level or better at audiometric octave frequencies between 125 and 8000 Hz. All listeners provided written informed consent, approved by the Ethics Committee of Carl von Ossietzky University of Oldenburg. Listeners received an hourly compensation for their participation.

The recording sentences were same as the official Mandarin Chinese matrix sentence test (CMNmatrix, Hu et al. 2018).

One hundred sentences (ten base lists of ten sentences) of the CMNmatrix were recorded from each speaker in both plain and Lombard speaking styles (each base list containing all 50 words). The 100 sentences were divided into 10 blocks of 10 sentences each, and the plain and Lombard blocks were presented in an alternating order. The recording took place in a double-walled, sound-attenuated booth fulfilling ISO 8253-3 (ISO 8253-3, 2012), using a Neumann 184 microphone with a cardioid characteristic (Georg Neumann GmbH, Berlin, Germany) and a Fireface UC soundcard (with a sampling rate of 44100 Hz and resolution of 16 bits). The recording procedure generally followed the procedures of Alghamdi et al. (2018). A Mandarin-native speaker and a phonetician participated in the recording session and listened to the sentences to control the pronunciations, intonation, and speaking rate. During the recording, the speaker was instructed to read the sentence presented on a frontal screen. In case of any mispronunciation or change in the intonation, the speaker was asked via the screen to repeat the sentence again, and on average, each sentence was recorded twice. In Lombard conditions the speaker was regularly asked via a prompt to repeat a sentence, to keep the speaker in the Lombard communication situation. For the plain-speech recording blocks, the speakers were asked to pronounce the sentences with natural intonation and accentuation, and at an intermediate speaking rate, which was facilitated by a progress bar on the screen. Furthermore, the speakers were asked to keep the speaking effort constant and to avoid any exaggerated pronunciations that could lead to unnatural speech cues. For the Lombard speech recording blocks, speakers were instructed to imagine a conversation to another person in a pub-like situation. During the whole recording session, speakers wore headphones (Sennheiser HDA200) that provided the audio signal of the speaker.. In the Lombard condition, the stationary speech-shaped noise ICRA1 (Dreschler et al., 2001) was mixed with the speaker’s audio signal at a level of 80 dB SPL (calibrated with a Brüel & Kjær (B&K) 4153 artificial ear, a B&K 4134 0.5-inch inch microphone, a B&K 2669 preamplifier, and a B&K 2610). Previous studies showed that this level induced a robust Lombard speech without the danger of inducing hearing damage (Alghamdi et al., 2018).

The sentences were cut from the recording, high-pass filtered (60 Hz cut-off frequency) and set to the average root-mean-square level of the original speech material of the Mandarin Matrix test (Hu et al., 2018). Then the best version of each sentence was chosen by native-Mandarin speakers regarding pronunciation, tempo, and intonation.

For more detailed information, please contact hongmei.hu@uni-oldenburg, sabine.hochmuth@uni-oldenburg.de.

Hu H, Xi X, Wong LLN, Hochmuth S, Warzybok A, Kollmeier B (2018) Construction and evaluation of the mandarin chinese matrix (cmnmatrix) sentence test for the assessment of speech recognition in noise. International Journal of Audiology 57:838-850. https://doi.org/10.1080/14992027.2018.1483083

This dataset contains 303 hours of Chinese-English mixed speech, collected from monologue based on given Chinese and English Mixed prompts, covering general and human-computer interaction domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(1,113 speakers), geographicly speaking, enhancing model performance in real and complex tasks like ASR, TTS, code-switching, and bilingual speech-related AI tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

This dataset includes recordings from 6 professional voice actors from Taiwan, covering news and colloquial speech. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.

Introduction

This Mandarin Chinese Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Mandarin-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Mandarin Chinese speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

The Mandarin Chinese Speecon database is divided into 2 sets: 1) The first set comprises the recordings of 550 adult Chinese speakers (276 males, 274 females), recorded over 4 microphone channels in 4 recording environments (office, entertainment, car, public place). 2) The second set comprises the recordings of 50 child Chinese speakers (26 boys, 24 girls), recorded over 4 microphone channels in 1 recording environment (children room). This database is partitioned into 26 DVDs (first set) and 3 DVDs (second set).The speech databases made within the Speecon project were validated by SPEX, the Netherlands, to assess their compliance with the Speecon format and content specifications.Each of the four speech channels is recorded at 16 kHz, 16 bit, uncompressed unsigned integers in Intel format (lo-hi byte order). To each signal file corresponds an ASCII SAM label file which contains the relevant descriptive information.Each speaker uttered the following items:Calibration data: 6 noise recordingsThe “silence word” recordingFree spontaneous items (adults only):2 minutes (session time) of free spontaneous, rich context items (story telling) (an open number of spontaneous topics out of a set of 30 topics)17 Elicited spontaneous items (adults only):3 dates, 2 times, 3 proper names, 2 city name, 1 letter sequence, 2 answers to questions, 3 telephone numbers, 1 language Read speech:30 phonetically rich sentences uttered by adults and 60 uttered by children5 phonetically rich words (adults only)4 isolated digits1 isolated digit sequence4 connected digit sequences1 telephone number3 natural numbers1 money amount2 time phrases (T1 : analogue, T2 : digital)3 dates (D1 : analogue, D2 : relative and general date, D3 : digital)3 letter sequences1 proper name2 city or street names2 questions2 special keyboard characters 1 Web address1 email address208 application specific words and phrases per session (adults)74 toy commands and 48 general commands (children)The following age distribution has been obtained: Adults: 224 speakers are between 15 and 30, 220 speakers are between 31 and 45, 106 speakers are between 46 and 60.Children: 17 speakers are between 8 and 10, 33 speakers are between 11 and 14.A pronunciation lexicon with a phonemic transcription in SAMPA is also included.

Mandarin Chinese(China) Spontaneous Dialogue 48KHZ Smartphone speech dataset, including at least 20 topics, covering a wide range of vocabulary and grammatical structures, encompassing various dialect regions of China, mirrors real-world interactions. Transcribed with text content, timestamp, speaker's ID and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The Taiwan Mandarin Speecon database is divided into 2 sets: 1) The first set comprises the recordings of 550 adult Taiwanese speakers (273 males, 277 females), recorded over 4 microphone channels in 4 recording environments (office, entertainment, car, public place). 2) The second set comprises the recordings of 50 child Taiwanese speakers (25 boys, 25 girls), recorded over 4 microphone channels in 1 recording environment (children room). This database is partitioned into 56 DVDs (first set) and 3 DVDs (second set).The speech databases made within the Speecon project were validated by SPEX, the Netherlands, to assess their compliance with the Speecon format and content specifications.Each of the four speech channels is recorded at 16 kHz, 16 bit, uncompressed unsigned integers in Intel format (lo-hi byte order). To each signal file corresponds an ASCII SAM label file which contains the relevant descriptive information.Each speaker uttered the following items:Calibration data: 6 noise recordings The “silence word” recordingFree spontaneous items (adults only):5 minutes (session time) of free spontaneous, rich context items (story telling) (an open number of spontaneous topics out of a set of 30 topics)17 Elicited spontaneous items (adults only):3 dates, 2 times, 3 proper names, 2 city names, 1 letter sequence, 2 answers to questions, 3 telephone numbers, 1 language Read speech:30 phonetically rich sentences uttered by adults and 60 uttered by children5 phonetically rich words (adults only)4 isolated digits1 isolated digit sequence4 connected digit sequences1 telephone number3 natural numbers1 money amount2 time phrases (T1 : analogue, T2 : digital)3 dates (D1 : analogue, D2 : relative and general date, D3 : digital)3 letter sequences1 proper name2 city or street names2 questions2 special keyboard characters 1 Web address1 email address46 core words synonyms208 application specific words and phrases per session (adults)74 toy commands, 14 phone commands and 34 general commands (children)The following age distribution has been obtained: Adults: 246 speakers are between 15 and 30, 235 speakers are between 31 and 45, 63 speakers are between 46 and 60, and 6 speakers are over 60.Children: 21 speakers are between 7 and 10, 29 speakers are between 11 and 14.A pronunciation lexicon with a phonemic transcription in SAMPA is also included.

This Mandarin Chinese speech synthesis dataset features with 294 speakers total 203 hours of audio, gender balanced 144 females and 150 males, ages from 18 to 60 years old. Each speaker records free-form dialogues based on given topics, and in each conversation, each person's audio is stored in their own separate WAV file. Professional linguists have annotated 16 types of paralanguage annotations, including text annotations and timestamps, and other information to accurately match the research and development needs of speech synthesis and paralanguage research.

Mandarin Stuttered Speech Dataset (StammerTalk)

The StammerTalk dataset contains 43 hours of spontenous conversations and reading of voice commands by 66 Mandarin Chinese speakers who stutter.

  Data Collection Process

The StammerTalk dataset was created by StammerTalk (口吃说) community (http://stammertalk.net/), in partnership with AImpower.org. Speech data collection was conducted by two StammerTalk volunteers, who also stutter, with participants over videoconferencing… See the full description on the dataset page: https://huggingface.co/datasets/AImpower/MandarinStutteredSpeech.

ID

King-ASR-018

  Duration

328 hours

  Recording Device

Desktop

  Description

This dataset was recorded in a quiet office environment, with 850 speakers participating, including 420 males and 430 females. All speakers involved in the recording were professionally selected to ensure standard pronunciation and clear articulation. The recorded text covers information such as news.

  URL… See the full description on the dataset page: https://huggingface.co/datasets/DataoceanAI/Dolphin_Model_Chinese-Mandarin-Speech-Recognition-Corpus.

Mandarin Full-Duplex Spontaneous Dialogue Speech Dataset, collected from dialogues based on given topics. Transcribed with text content, speaker's ID, gender, age and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Mandarin -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Mandarin Chinese speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Recently, considerable attention has been given to the effect of the age of acquisition (AoA) on learning a second language (L2); however, the scarcity of L2 AoA ratings has limited advancements in this field. We presented the ratings of L2 AoA in late, unbalanced Chinese-English bilingual speakers and collected the familiarity of the L2 and the corresponding Chinese translations of English words. In addition, to promote the cross-language comparison and motivate the AoA research on Chinese two-character words, data on AoA, familiarity, and concreteness of the first language (L1) were also collected from Chinese native speakers. We first reported the reliability of each rated variable. Then, we described the validity by the following three steps: the distributions of each rated variable were described, the correlations between these variables were calculated, and regression analyses were run. The results showed that AoA, familiarity, and concreteness were all significant predictors of lexical decision times. The word database can be used by researchers who are interested in AoA, familiarity, and concreteness in both the L1 and L2 of late, unbalanced Chinese-English bilingual speakers. The full database is freely available for research purposes.