How to ensure voice data quality for AI: From QA to QC

The quality of input data is always the key factor determining the effectiveness of any AI project involving voice. A low-quality dataset not only makes the model’s learning process difficult but can also result in faulty products, negatively impacting the end-user experience. Therefore, voice data quality checking (QA/QC) is an essential step in any project, from training virtual assistants and speech synthesis to voice authentication or building emotion recognition systems.

Establishing a professional QA/QC process helps eliminate erroneous, noisy, or non-standard files while ensuring the final dataset always meets the strictest audio standards. This article will cover the steps, criteria, and essential tools for checking the quality of this data type.

Voice data quality evaluation criteria

Signal-to-Noise Ratio (SNR)

SNR is a measurable parameter used in science and engineering to compare the level of the desired signal to the background noise level, usually represented in decibels (dB). It is a crucial indicator reflecting how “clean” a recording is. The higher the SNR, the more prominent the voice signal and the less noise contamination.

Example: A recording with an SNR of 25dB usually ensures that only the human voice is clear, with little background noise. In contrast, a file with only 10dB SNR may allow you to easily hear fans or traffic noise, reducing AI recognition quality.

Clarity and accurate pronunciation

The checking process must ensure that listeners—be they AI or humans—can clearly understand the content.

Example: A recording of the sentence “I want to book a flight ticket” must ensure all syllables like “book”, “a”, “flight” are not swallowed, distorted, or mispronounced into “booking” or “butterfly”.

No background noise, no overpowering environmental sounds

Recordings must be clean, without noise such as car horns, fan sounds, phone rings, etc.

Example: If you listen back to a file and find barking dogs, honking horns, or an alarm clock in the mix, that file will fail in the voice data quality checking process.

File format and technical parameter compliance

Audio files must consistently adhere to standards on sampling rate (e.g., 16 kHz or 44.1 kHz), bit rate, format (wav, mp3…), and metadata normalization.

Example: If a project requires .wav 16 kHz files only, any recording sent in as MP3 format or 8 kHz sampling rate will be eliminated immediately.

Adequate duration, no clipping or missing segments

During the voice data quality checking process, you must catch files with missing starts, cut-off endings, or segments improperly spliced together.

Example: The sentence “Hello, I am a virtual assistant” recorded as only “Hello, I am a virtual” means the start is missing, not meeting adequate duration or full-content requirements.

Additional criteria (depending on the project)

Sometimes there are requirements on dialect, emotion, and microphone quality—all must be annotated, scored, and reported during QA.

Example:  If the project requires American English accents, but receives a file spoken with a strong British accent, it will be marked as a dialect error. Or if the script asks for a “happy” tone but the recording is performed with a sad tone, it will be rejected.

>> You might be interested in: Challenges in collecting diverse voice data

Steps for checking voice data quality

Automated checking

• Use tools to analyze SNR, detect noise, and check technical file parameters.
• Filter out files with unmatched metadata, wrong formats, excessive or insufficient duration.
• Automated voice data quality checking saves time and detects common errors at scale.

Manual reviewing

• Listen to some or all files to assess clarity, pronunciation, emotion, dialect, etc.
• Compare the content to the script or transcript, ensuring no minor errors go unchecked.
• Record issues and label files for corrections or retakes as needed.

Integrated checking

• Use real-time warnings, instructions at the recording moment, enabling participants to correct errors instantly.
• Voice data quality checking can occur in parallel with the recording process, boosting efficiency and conserving resources.

Multi-round review

• Multiple experts perform cross-checks and overlapping reviews to eliminate subjective errors.
• Build an evaluation checklist for each voice data quality checking criterion to ensure consistency throughout the process.

Random sampling & real-world testing

• Randomly report or extract samples to test in an AI model.
• Gather real feedback to further optimize the voice data quality checking process.

>> You might be interested in: Popular voice data collection methods

Tools supporting voice data quality checking

To increase accuracy and save time, businesses should use solutions/software to support voice data quality checking, such as:

• SNR measurement and audio analysis tools: Praat, Audacity, Adobe Audition, Wavesurfer, custom Python scripts.
• Noise/anomaly and mispronunciation detection software: AI-based noise detection, sound anomaly detection.
• Integrated voice data QA management systems: Logging, scoring, version control, reviewer management.

>> You might be interested in: Common types of audio data annotation

Ensuring voice data quality with BPO.MP’s optimal process

For every AI project where voice data is foundational, voice data quality checking is the decisive step in building intelligent, precise, and reliable systems. A systematic QA/QC process, skillfully combining cutting-edge technology with expert evaluation, is the launchpad to ensure your data meets global standards, thus optimizing model effectiveness and delivering an exceptional experience to end users.

Are you seeking to establish a voice data quality checking process for your project? Are you unsure about standards, tools, or deep review solutions?

Let BPO.MP be your trusted partner! We deliver comprehensive, flexible voice data quality checking services tailored to any project need, ensuring every file meets international standards, optimizing resources, reducing costs, and elevating the value of your AI products.