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Version: 3.4.0

Keyword Spotting vs. Speech to Text

Keyword Spotting (KWS) and Speech to Text (STT) are both technologies related to speech processing, but they serve different purposes and offer distinct advantages. Here’s a breakdown of their differences and benefits.

Keyword Spotting (KWS)​

Keyword spotting is a technology that listens for specific words or phrases within a continuous stream of audio. It detects and identifies these predefined keywords, ignoring the rest of the speech.

Benefits:​

  1. Efficiency: KWS systems are designed to detect specific words or phrases quickly and efficiently, making them highly responsive. The technology searches the recording and returns a list of detected keywords along with the score and confidence for each. The score is a numerical expression of the likelihood that a word was said within a specified time frame.

  2. Low Resource Consumption: KWS typically requires less computational power compared to full STT systems, making it suitable for devices with limited resources.

  3. Real-Time Interactions: Ideal for applications that require immediate responses to specific interactions.

Speech to Text (STT)​

Phonexia Speech to Text converts spoken language into written text. It transcribes entire sentences and conversations, capturing all spoken words.

Benefits:​

  1. Comprehensive Transcription: STT provides a full transcription of spoken language, which is useful for documentation, subtitles, transcription services, and any application needing detailed records of conversations.

  2. Versatility: STT can be used in a wide range of applications, from voice

  3. Versatility: STT can be used in a wide range of applications, from voice dictation and automated transcription services to voice commands and interactive voice response (IVR) systems.

  4. Context Understanding: By transcribing complete sentences, STT systems can better understand the context and nuances of speech, which is critical for more complex voice interactions and commands.

  5. Accessibility: STT technology helps make content accessible to people

  6. Accessibility: STT technology helps make content accessible to people with hearing impairments by converting spoken language into readable text.

  7. Data Analysis: Full transcriptions can be analyzed for insights, sentiment analysis, and other advanced data processing tasks, providing valuable information from spoken content.

Key differences​

Scope of functionality:

  • KWS is limited to detecting specific keywords or phrases. The number of keywords and pronunciations is not limited, and keywords are not dependent on any dictionary.
  • STT transcribes the entire speech content. The technology can be adapted at two levels: the acoustic model or the language model. Adapting the acoustic model to speakers from a specific region or dialect involves creating a new acoustic model. The language model can be more easily adapted, especially when certain words are missing (such words can never appear in the transcription), such as terms from a specific business domain.

Resource requirements​

KWS generally requires fewer resources and can operate efficiently on lower-powered hardware. The performance may drop only when processing keyword lists without explicitly defined pronunciations. In such cases, the technology must create pronunciations internally before starting the processing, which takes additional time—the more keywords lacking predefined pronunciations, the longer the delay before processing begins. When the keyword list has pronunciations defined for each keyword, even thousands of defined keywords have no impact on performance.

STT typically requires more computational power, especially for high accuracy and context-aware transcription. Depending on the version, it can process from 1,800 to 3,700 hours of audio (with 50% of the audio being speech) in one day on a single server CPU with 8 cores.

Use cases:

  • KWS: Ideal for maintaining fast reaction times by routing calls with specific content to human operators or searching for specific information in large call archives. It can also route multimedia files and streams according to their content.
  • STT: Suitable for dictation, transcription, creating text records of conversations, and detailed voice interactions. It can also be used for searching specific information in large call archives or maintaining high reaction times by routing calls with specific content/topics to human operators.

Summary​

In summary, Keyword Spotting and Speech to Text technologies serve distinct purposes in speech processing. KWS is optimized for detecting specific words with high efficiency and low resource consumption, making it perfect for wake words and command recognition. STT, on the other hand, provides comprehensive transcriptions of spoken language, making it suitable for documentation, accessibility, and detailed voice interaction analysis. Each technology has unique benefits and is chosen based on the specific needs of the application.