InfoBayAI/English-United-States-Call-Center-Audio-Dataset-Single-Channel

Dataset Description

This dataset is a large-scale collection of 127,624 hours of processed English (US) single-channel call center audio recordings, derived from a broader corpus of 2,065,026 hours of processed call center audio data spanning 32 languages, designed for the development, training, and evaluation of advanced Speech AI, Conversational AI, ASR, STT, SFT, and RLHF systems.

The dataset consists of real-world customer service and support conversations collected from operational call center environments. Audio is provided in a single-channel format, where conversational speech is captured as a unified audio stream, closely reflecting real-world telephony deployments and production speech processing pipelines.

This dataset is particularly valuable for building scalable enterprise-grade AI systems including Automatic Speech Recognition (ASR), Speech-to-Text (STT), Call Analytics, Conversational Intelligence, Sentiment Analysis, Large Language Model (LLM) training pipelines, Supervised Fine-Tuning (SFT), and Reinforcement Learning with Human Feedback (RLHF).

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Audio Processing & Refinement Pipeline

To ensure enterprise-grade quality and usability, the dataset undergoes a comprehensive 4-step audio refining and processing pipeline before final delivery:

1. Duplicate Asset Elimination

Removal of duplicate or repeated recordings to maintain dataset uniqueness, consistency, and high-quality training data.

2. Low-Activity Voice Removal

Filtering of silent, low-volume, inactive, or low-quality audio samples to improve overall dataset reliability.

3. PII Detection & Muting

Automatic detection and redaction/muting of personally identifiable information (PII) to support privacy compliance and safe AI training.

4. Background Noise Removal

Application of advanced noise-reduction and audio-cleaning techniques to enhance speech clarity and improve model performance.

This processing pipeline ensures that the dataset is clean, scalable, production-ready, and optimized for speech AI, conversational AI, ASR, SFT, and RLHF workflows.

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Dataset Specification

• Duration: 127,624 Hours
• Language: English (US)
• Type: Processed
• Audio Conditions: Real-world call center environments
• Channel Configuration: Single Channel
• Format: .wav, .mp3, .ogg, etc.
• Sampling Rate: 8000 Hz

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Key Use Cases

• Automatic Speech Recognition (ASR)
• Speech-to-Text (STT) Systems
• Call Center AI Applications
• Contact Center Analytics
• Conversational AI Training
• Large Language Model (LLM) Fine-Tuning
• Supervised Fine-Tuning (SFT)
• Reinforcement Learning with Human Feedback (RLHF)
• Sentiment Analysis
• Emotion Detection
• Quality Monitoring
• Compliance Monitoring
• Customer Experience Analytics
• Virtual Customer Support Agents
• Voice AI Applications

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Value of Single Channel Dataset

• Closely reflects real-world telephony and call center deployments
• Ideal for speech recognition and transcription model training
• Supports conversational AI and dialogue understanding systems
• Captures natural customer service interaction patterns
• Suitable for large-scale AI training pipelines
• Effective for sentiment and behavioral analytics
• Useful for enterprise contact center intelligence platforms
• Supports downstream NLP and LLM workflows

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Audio Quality Analysis

Signal Quality Analysis (Signal QA)

To ensure robust signal-level integrity and consistency, the dataset was evaluated using multiple acoustic and signal-processing metrics.

Metric	Value	Interpretation
Average SNR (dB)	50.03	High signal-to-noise ratio indicating clean audio with minimal background noise
Average RMS Energy	0.089	Stable signal energy level suitable for speech processing
Silence Ratio	0.448	Reflects natural conversational pauses
Clipping Ratio	0.0	No clipping detected, ensuring distortion-free audio
Loudness (LUFS)	-22.12	Well-balanced loudness within acceptable speech ranges
Overall Quality Score	70.83	Good signal quality suitable for training and evaluation

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DNSMOS Evaluation

To ensure production-level reliability, the dataset was evaluated using DNSMOS (Deep Noise Suppression Mean Opinion Score).

Metric	Score	Interpretation
Speech Quality (SIG)	3.89	Clear and intelligible conversational speech
Background Noise (BAK)	4.01	Strong noise suppression and acoustic clarity
Overall MOS (OVR)	3.81	High-quality real-world audio suitable for model training

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SQUIM-Based Audio Quality Analysis

To further assess perceptual and signal characteristics, the dataset was evaluated using SQUIM-based metrics.

Metric	Value	Interpretation
Average Energy	0.003	Controlled signal amplitude without distortion
Spectral Flatness	0.052	Speech-dominant signal with minimal noise characteristics
Zero Crossing Rate (ZCR)	0.062	Consistent with voiced speech and low high-frequency noise
Dynamic Range	1.683	Natural conversational amplitude variation
SI-SDR Proxy	15.0	Good signal-to-distortion ratio
SQUIM Score	62.59	Strong perceptual quality for real-world applications

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Key Insight

The dataset maintains strong acoustic quality despite real-world call center conditions, making it suitable for production-grade Speech AI systems, Conversational AI platforms, LLM pipelines, speech understanding models, and enterprise customer support applications.

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Dataset Validation via End-to-End Model Training

To validate dataset effectiveness, a complete speech-to-NLP training pipeline was built and executed using InfoBay.AI audio data.

Full Pipeline

OpenAI Whisper Transcription

→ Sentiment Labeling

→ DistilBERT Training

→ 3-Class Sentiment Classification

Validation Insight

This end-to-end workflow demonstrates that the dataset is not only large-scale but also self-sufficient for developing downstream AI applications without reliance on external datasets.

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Sentiment Classification Task

The dataset supports supervised learning for sentiment understanding across three classes:

• Negative (Class 0)
• Neutral (Class 1)
• Positive (Class 2)

The dataset contains naturally occurring emotional and contextual variation, making it highly suitable for:

• RLHF Preference Modeling
• Emotion-Aware Conversational Agents
• Human-Aligned Response Generation
• Customer Experience Analytics
• Voice-Based Sentiment Intelligence

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Model Performance (From-Scratch Training)

A DistilBERT-based model trained from scratch achieved strong performance using transcripts generated from this dataset.

• Accuracy: ~98%
• Macro F1 Score: ~0.98
• Weighted F1 Score: ~0.99

Class	Sentiment	Precision	Recall	F1-score	Support
0	Negative	0.97	0.96	0.96	1,128
1	Neutral	0.99	0.99	0.99	7,865
2	Positive	0.98	0.98	0.98	2,658

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Basic JSON Schema

{
  "id": "string",
  "audio_filepath": "string",
  "duration": "float",
  "language": "string",
  "sample_rate": "integer",
  "format": "string",
  "num_speakers": "integer",
  "domain": "call_center",
  "metadata": {
    "source": "string",
    "recording_condition": "string"
  }
}

Data Creation

Procured through formal agreements and generated in the ordinary course of business.

Considerations

This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website InfoBay.AI or contact us directly.

-Ph: (91) 8303174762
-Email: datareq@infobay.ai