Audio Speech Analysis

What is audio speech analysis?
Machine learning researchers are on a mission to help machines process audio information just as humans do. There are an increasing number of useful machine learning applications in the realm of speech processing, including the automotive industry, healthcare and military amongst others.

As with all unstructured data formats, audio has a couple of preprocessing steps which have to be followed before it is presented for analysis. When training your automatic speech recognition (ASR) system, you need large volumes of accurate language data to ensure that your system can understand and respond to human speech no matter what environment or context it’s in. You will also need large volumes of data to train your machine learning model effectively.

Why Lionbridge?

To build any machine learning system, a high quality data with ground truth is needed to train and evaluate the performance. Lionbridge can help generate large volumes of data for all types of media (images, videos, audio & text) quickly and cost effectively. Our team of 500,000+ language professionals can help gauge speaker fluency, determine intent or sentiment, conduct audio categorization and more.

Scale

With 500,000+ native workers in 300 languages across all major time zones, we can comfortably keep pace with your needs, no matter the volume.

Quality

Professional, accurate and consistent work is completed by certified crowdworkers – always.

Value

We offer clear, competitive pricing depending on the volume and language(s) you need.

Multilingual Audio Speech Analysis Services

Lionbridge provides professional audio analysis services in 300 languages. Some of our most popular languages include:
  • Chinese audio analysis
  • Dutch audio analysis
  • French audio analysis
  • German audio analysis
  • Italian audio analysis
  • Japanese audio analysis
  • Portuguese audio analysis
  • Spanish audio analysis
Customer case study

For a firm that required native speakers across multiple languages, our team of native speakers evaluated hundreds of machine-generated speech samples. Contributors identified pronunciation and flagged any errors to determine overall naturalness.