AI Agent Engineer

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Speech Recognition

5 min read

Speech Recognition

Course overview

Speech recognition, or Automatic Speech Recognition (ASR), converts linguistic content in audio into text. In Agent engineering, a usable result is normally more than a full transcript: it includes segments, timestamps, language, anonymous speaker labels, failure reasons, and audio provenance. Each processing run should join asset_id, source_revision, acquisition and analysis formats, timestamp reference, model/decoder/normalization versions, and segment_id + revision into one evidence chain. Without it, “the same transcript segment” cannot be reproduced, corrected, or deleted.

This course is for learners without audio or deep-learning background. Dynamic material was checked on 2026-07-22. Model commands, supported languages, upload limits, and hardware requirements can change; the course does not treat a repository’s or vendor’s current options as a permanent interface.

Where this fits in the overall path

Speech recognition is in the Extended applications and complex collaboration stage and provides the input channel for a voice Agent. Recognized text can flow into Context Engineering, RAG, and Workflow Automation. Together with Text to Speech, it forms the input/output modules; Real-Time Multimodal Interaction then handles turns, interruptions, tools, and session recovery.

flowchart LR
    A[Audio asset\nasset_id + source_revision] --> B[Decode and format contract\ntime reference, sample rate, channels]
    B --> C[Segmentation and recognition\nsegment_id + revision]
    C --> D[Committed transcript\nmodel, decode, normalization versions]
    D --> E[Retrieval, summarization, or Agent runtime]
    E --> F[Tools or high-risk actions]
    G[Independent identity, policy, and approval] -. gate .-> F

The transcript in this diagram is always untrusted user input. It can support understanding and retrieval, but cannot independently authorize tools, side effects, or identity decisions. Real-time turns, interruption, and action gates are developed separately in Real-Time Multimodal Interaction.

Learning objectives

  • Explain the minimal intuition for waveforms, sample rate, channels, frames, and spectra.
  • Distinguish the responsibilities and boundaries of conventional acoustic/language modules and end-to-end ASR.
  • Design a data contract for VAD, segmentation, timestamps, speakers, and post-processing.
  • Distinguish acquisition format, model-analysis format, and subtitle/transcript output format, including time coordinates and revision semantics.
  • Calculate WER/CER correctly and inspect quality differences by environment, language, or group slices.
  • Compare batch and streaming architectures, and design backpressure, unknown terminal state, privacy, and human review.
  • Complete a fully offline synthetic transcript-evaluation project.

Prerequisites

  1. Audio digitization and the ASR pipeline: learn waveforms, sampling, and the output contract.
  2. Acoustic, language, and end-to-end intuition: understand what different model routes do.
  3. Data annotation and splitting: establish transcription rules and a leakage-free dataset.
  4. VAD, segmentation, timestamps, and speakers: turn a continuous meeting into usable segments.
  5. Batch, streaming, and post-processing: design real-time/offline services and a stable text layer.
  6. WER, CER, and slice evaluation: measure error under consistent rules.
  7. Fairness, privacy, deployment, and troubleshooting: cover data boundaries and group differences.
  8. Project: offline transcript evaluation: run fixture evaluation and interpret the report.

Hands-on entry point

Mastery criteria

  • I can explain sample rate, channels, frames, VAD, and speaker diarization.
  • I can distinguish recognizing content, identifying who is speaking, and verifying a speaker’s identity.
  • I can define partial/final result semantics and revision rules for streaming recognition.
  • I can calculate WER/CER by hand and explain how normalization, tokenization, and slicing affect the metric.
  • I can design runtime metrics and a controlled diagnostic process that do not record raw audio body content.
  • I can run the project, locate timestamp/text problems, and add an anonymous slice.

Connections to other knowledge bases

Primary references

The following sources were checked on 2026-07-22:

Boundary between facts and recommendations

Whisper, cloud transcription APIs, and real-time sessions are concrete implementations. The asset chain, slicing, logging, and review practices in this course are engineering recommendations that must be validated against your languages, devices, setting, budget, and compliance needs. No model or transcript alone can prove a speaker’s real-world identity or consent.

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