Spanish (Spain) Call Center Speech Dataset for Healthcare

The audio dataset comprises call center conversations for the Healthcare domain, featuring native Spanish speakers from Spain. It includes speech data, detailed metadata and accurate transcriptions.

Category

Unscripted Call Center Conversations

Total Volume

30 Speech Hours

Last updated

Jun 2024

Number of participants

60

Machine learning voice data for Healthcare call center in Spanish (Argentina)
Download
Download Icon

About this Off-the-shelf Speech Dataset

Card Head Line

Introduction

Welcome to the Spanish Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

  • Participant Diversity:
  • Speakers: 60 expert native Spanish speakers from the FutureBeeAI Community.
  • Regions: Different states/provinces of Spain, ensuring a balanced representation of Spanish accents, dialects, and demographics.
  • Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.
  • Recording Details:
  • Conversation Nature: Unscripted and spontaneous conversations between call center agents and customers.
  • Call Duration: Average duration of 5 to 15 minutes per call.
  • Formats: WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 and 16 kHz.
  • Environment: Without background noise and without echo.
  • Topic Diversity

    This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

  • Inbound Calls:
  • Appointment Scheduling
  • New Patient Registration
  • Surgery Consultation
  • Consultation regarding Diet, and many more
  • Outbound Calls:
  • Appointment Reminder
  • Health and Wellness Subscription Programs
  • Lab Tests Results
  • Health Risk Assessments
  • Preventive Care Reminders, and many more
  • This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

    Transcription

    To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

  • Speaker-wise Segmentation: Time-coded segments for both agents and customers.
  • Non-Speech Labels: Tags and labels for non-speech elements.
  • Word Error Rate: Word error rate is less than 5% thanks to the dual layer of QA.
  • These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Spanish language.

    Metadata

    The dataset provides comprehensive metadata for each conversation and participant:

  • Participant Metadata: Unique identifier, age, gender, country, state, district, accent and dialect.
  • Conversation Metadata: Domain, topic, call type, outcome/sentiment, bit depth, and sample rate.
  • This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Spanish call center speech recognition models.

    Usage and Applications

    This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

  • Speech Recognition Models: Training and fine-tuning speech recognition models for Spanish.
  • Speech Analytics Models: Building speech analytics models to extract insights, identify patterns, and glean valuable information from customer conversation, enables data-driven decision-making and process optimization within the Healthcare sector.
  • Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Healthcare industries.
  • Sentiment Analysis: Analyzing customer sentiment and improving customer experience based on call center interactions.
  • Generative AI: Training generative AI models capable of generating human-like responses, summaries, or content tailored to the Healthcare domain.
  • Secure and Ethical Collection

  • Our proprietary data collection and transcription platform, “Yugo” was used throughout the process of this dataset creation.
  • Throughout the data collection process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.
  • The data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
  • It does not include any personally identifiable information about any participant, which makes the dataset safe to use.
  • The dataset does not contain any copyrighted content.
  • Updates and Customization

    Understanding the importance of diverse environments for robust ASR models, our call center voice dataset is regularly updated with new audio data captured in various real-world conditions.

  • Customization & Custom Collection Options:
  • Environmental Conditions: Custom collection in specific environmental conditions upon request.
  • Sample Rates: Customizable from 8kHz to 48kHz.
  • Transcription Customization: Tailored to specific guidelines and requirements.
  • License

    This Healthcare domain call center audio dataset is created by FutureBeeAI and is available for commercial use.

    Use Cases

    Use of speech data in Conversational AI

    Call Center Conversational AI

    Use of speech data for Automatic Speech Recognition

    ASR

    Use of speech data for Chatbot & voicebot creation

    Chatbot

    Use of speech data in Language Modeling

    Language Modelling

    Use of speech data in Text-into-speech

    TTS

    Speech data usecase in Speech Analytics

    Speech Analytics

    Dataset Sample(s)

    Card Head Line
    00:00

    ATTRIBUTES

    TRANSCRIPTION

    TIME
    TRANSCRIPT
    1.112 - 2.156
    Hey Futurebee
    3.443 - 3.601
    -
    4.242 - 4.360
    -
    4.541 - 5.484
    Hey Futurebee.
    9.567 - 12.214
    Hola, buenos días. ¿El señor <PII>Antonio López</PII>?
    13.022 - 15.176
    Sí, sí, soy yo. ¿Qué tal? Buenos días.
    15.371 - 19.376
    Buenos días. Le llamo de la clínica Iderma.
    20.274 - 27.596
    [filler] Probablemente no le sonará nuestro nombre, pero estamos azo~ asociados con el centro de fisioterapia donde acude usted habitualmente.
    28.050 - 28.442
    Ya.
    29.094 - 37.789
    Entonces, le quería informar que hemos creado un nuevo concepto de clínica. Vamos a inaugurar [filler] próximamente.
    38.179 - 51.557
    Entones, como cliente del centro de fisioterapia, le queríamos explicar un poco el este nuevo este nuevo concepto de clínica y [filler] invitarlo a que nos conozca personalmente el día que inauguremos las instalaciones.
    49.960 - 50.219
    -
    51.960 - 52.411
    Sí.
    52.231 - 53.526
    [filler] y
    52.859 - 61.750
    A ver, un momentico. ¿Y estas instalaciones [filler] el centro este nuevo, esta nueva clínica, en qué sitio? ¿Donde? ¿Qué es aquí en en Barcelona?
    62.216 - 68.683
    Está en Barcelona, sí. Muy cerca del del centro de fisioterapia donde donde usted era cliente.
    68.938 - 69.248
    Sí.
    69.795 - 79.831
    De momento ese centro lo vamos a mantener pero en en la nueva clínica, ya le digo, es un concepto más global. Va a haber muchos más servicios, a parte de la fisioterapia.
    80.245 - 90.302
    Y usted puede puede acudir o puede combinar los tratamientos, porque algunos no van a estar en en los dos sitios, los vamos a tener en exclusiva en la clínica nueva.
    90.492 - 90.796
    -
    90.989 - 98.250
    [filler] Bueno, a parte de fisioterapia que es lo que usted, bueno [filler], te~ le tenemos en en ficha como cliente.
    98.724 - 111.287
    Vamos a tener servicios de nutrición, servicios de estética, medicina de varios ámbitos. [filler] No sé si usted hace uso de estos servicios en otros centros, quizá.
    111.424 - 111.864
    -
    111.543 - 114.992
    Esta sería la manera de tenerlos un poco todos en el mismo sitio.
    115.638 - 115.778
    -
    115.656 - 115.953
    Sí.
    116.308 - 116.801
    Qué
    116.378 - 121.149
    No, me puede, me puede interesar, porque a parte de la fisioterapia que
    121.263 - 121.462
    -
    121.700 - 122.742
    ya me va muy bien,
    123.236 - 126.465
    [noise] a esta, nutrición sí que me interesaría
    127.075 - 136.020
    porque así podía combinar pues el tema este para mantener un poco el estado físico bien y tal, ¿no? Porque todo va acompañado.
    135.078 - 135.436
    [filler]
    136.717 - 144.008
    Pero, bueno, por eso te preguntaba antes si estaba en la en la zona. Si ya me dices que sí, pues sí, sí, puedo estar interesado.
    144.519 - 149.776
    Sí, en l~ en concreto lo que me comenta de servicios de nutrición, tenemos asesores
    149.842 - 150.181
    -
    150.366 - 157.990
    tanto si existe una patología, por ejemplo problemas de de de peso de o que estén interesados en bajar de peso,
    158.497 - 165.747
    tanto personas que quieran, por ejemplo ganar masa muscular combinado con con entrenamiento físico,
    166.548 - 170.550
    una dieta especial, por ejemplo para deportistas o para gente que necesita
    170.967 - 174.078
    [filler] pues, o que tiene un consumo de energía más elevado,
    174.681 - 180.252
    [filler] tenemos, bueno, varios varios aspectos que se podrían tocar. No sé cuál cuál sería su caso en concreto.
    179.133 - 185.443
    Vale. No, pero es que puede ser interesante, porque el hecho de bajar de peso pues bueno, es uno de mis problemas, ¿no?
    180.991 - 181.544
    Pero (()).

    Dataset Details

    Card Head Line

    Language

    Spanish

    Language code

    es

    Country

    Spain

    Accents

    Castellano del Norte, Castellano del Sur ...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70

    File Details

    Card Head Line

    Environment

    Silent, Noisy

    Bit Depth

    16 bit

    Format

    wav

    Sample rate

    8khz & 16khz

    Channel

    Stereo

    Audio file duration

    5-15 minutes

    Need datasets for a specific AI/ML use case?
    Don't worry, we've got you covered! 👍

    Contact Us
    Prompt 2 Bg