Audio & Speech Services

Audio & Speech Services focus on collecting, transcribing, and annotating high-quality speech data to enhance AI models for voice recognition, speech synthesis, and audio-based applications. These services are crucial for training virtual assistants, call center automation, and multilingual speech AI.

Audio Annotation & Tagging

Involves labeling audio files with metadata (e.g., background noise, speaker identity) to train AI in understanding sound patterns. Workers tag clips for context or features, enhancing audio processing capabilities.

Emotion & Sentiment Speech Labeling

Focuses on identifying emotional tones in speech (e.g., happy, angry, neutral). Annotators label audio segments with sentiment or intensity, enabling AI to interpret vocal cues.

Multilingual Speech Dataset Creation

Entails collecting and annotating speech data across multiple languages. Workers record, transcribe, or tag audio, building datasets for global speech recognition systems.

Speaker Verification & Authentication

Workers label audio to train AI in distinguishing individual voices (e.g., “same speaker” vs. “different speaker”). This supports biometric security or personalized AI applications.

Speech Data Augmentation

Involves modifying audio (e.g., adding noise, altering pitch) to create varied samples. Workers generate and validate augmented data, improving AI robustness across conditions.

Speech Segmentation

Workers divide audio into meaningful units (e.g., words, sentences, pauses), tagging boundaries to train AI in parsing continuous speech accurately.

Speech Transcription

Entails converting spoken audio into text. Transcribers produce accurate transcripts, often with timestamps, to align speech with written data for AI training.

Text-to-Speech (TTS) Training Data

Workers create paired text and audio datasets, recording natural speech to match scripts. This trains AI to generate human-like synthetic voices.

Voice Recording & Creation

Involves producing original audio samples (e.g., scripted readings, spontaneous speech). Workers record diverse voices, ensuring datasets reflect varied accents, ages, or styles.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the development and refinement of Audio & Speech AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the audio data that powers these systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand audio standards, linguistic nuances, and project goals. For instance, in emotion labeling, PMs might train annotators to recognize subtle vocal shifts, using sample clips and scoring guides. Onboarding includes practical tasks like transcribing or segmenting audio, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also set up workflows, such as multi-step reviews for multilingual datasets.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., transcribing 10,000 minutes of speech) and set metrics like accuracy, consistency, or noise tolerance. They monitor progress via dashboards, address delays, and refine guidelines based on worker feedback or client priorities.

Collaboration with AI Teams

PMs bridge the gap between audio curators and machine learning engineers, translating technical requirements (e.g., sample rate specifications) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI training cycles.

We Manage the Tasks Performed by Workers

The annotators, transcribers, or recorders perform the detailed work of preparing high-quality audio datasets. Their efforts are precise and auditory-focused, requiring attention to sound and context.

Common tasks include:

Labeling and Tagging

For audio annotation, we might tag a clip with “crowd noise” or “male speaker.” In speaker verification, they label pairs as “match” or “no match.”

Contextual Analysis

For emotion labeling, our team assesses vocal tone, tagging a segment as “frustrated” or “calm.” In speech segmentation, they identify pauses or intonation shifts.

Flagging Violations

In transcription, our employees and subcontractors flag unclear audio (e.g., overlapping voices), ensuring only reliable data is used. In TTS training, they mark unnatural readings.

Edge Case Resolution

We tackle tricky cases—like heavily accented speech or distorted recordings—often requiring discussion or escalation to audio specialists.

We can quickly adapt to and operate within our clients’ audio platforms, such as proprietary annotation tools or industry-standard systems, efficiently processing batches of audio ranging from dozens to thousands of clips per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated audio data required to train and enhance Audio & Speech AI systems is significant, driven by the diversity of sound and language. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 5,000–20,000 labeled audio samples per category (e.g., 20,000 emotion-tagged clips). For multilingual speech, this could double to cover languages.

Iterative Refinement

To boost accuracy (e.g., from 80% to 95%), an additional 3,000–10,000 samples per issue (e.g., misrecognized accents) are often needed. For example, refining TTS might demand 5,000 new recordings.

Scale for Robustness

Large-scale systems (e.g., global voice assistants) require datasets in the hundreds of thousands to handle rare dialects, background noises, or speaker variations. A speech recognition model might start with 50,000 minutes, expanding by 20,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain audio for review. This reduces volume but requires ongoing curation—perhaps 500–2,000 samples weekly—to maintain performance.

The scale necessitates distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and quality.

Multilingual & Multicultural Audio & Speech Services

We can assist you with your audio and speech service needs across diverse linguistic and cultural landscapes.

Our team is equipped to curate and process audio data for global applications, ensuring accurate and culturally relevant datasets tailored to your goals.

We work in the following languages: