What is Speech Analysis?

March 15, 2022
People Intelligence
Voice Analytics : What does your voice.
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Speech analysis is the process of transcribing a recorded conversation (such as a conversation between an employee and customer at a business) and analyzing it to derive valuable insights. It can be best viewed as a three-step process:

  • Step 1: Data Processing. A phone conversation is recorded and then transcribed. The transcription can be done manually, or (much more likely nowadays) automatic speech recognition software can be employed to transcribe the audio file.
  • Step 2: Analysis. The data generated in step 1 is analyzed according to pre-determined criteria. The team performing the analysis may be interested in searching for specific keywords spoken by one or both speakers, sentiments displayed by one or both speakers, or many other categories of analysis. The step can be done manually, or software can be utilized to perform specific types of analysis.
  • Step 3: Insights. A detailed report on the analysis is then delivered, providing the analysis team with valuable insights about the conversation and the speaker(s).

There are countless benefits available to businesses that employ speech analysis to review recorded conversations like sales calls or customer service calls. A business could ensure compliance statements are being delivered correctly, identify trending complaints, monitor key performance indicators like average response time, and much more.

The Impact of A.I.

Quality speech analysis first requires a quality transcription of the recorded dialogue. While manual transcription was the only option decades ago, the transcription process has been automated today thanks to increasingly accurate speech recognition software made possible by artificial intelligence.

Artificial intelligence refers to the ability of machines to think like humans and act like humans. A subset of artificial intelligence is machine learning, which is the ability of a computer system to learn on its own. This can be accomplished by providing an algorithm with large amounts of data and then teaching the system to recognize patterns within that data. This means the algorithm can grow more robust and more intelligent over time without human intervention.

Artificial intelligence has allowed for impressive advancements in speech recognition because machines have become much adept at recognizing, interpreting, and analyzing human speech in the same way that humans do. Researchers and software engineers have had to overcome many hurdles along this long road of developing more accurate speech recognition software. Humans do not always speak clearly and concisely, and we often employ shortened words and phrases like "I dunno" instead of "I don't know and non-word vocal expressions like "uh-huh" to convey "yes" during our speech. Additionally, speakers of the same language may have regional dialects or accents that alter the pronunciation of the same word, which means one comment from one language could be pronounced in many ways. Although these issues have not been addressed entirely, modern speech recognition software has evolved tremendously since its early days. Technology like Automatic Speech Recognition (ASR) have become quite sophisticated and can accurately convert spoken word to text, while Natural Language Processing can process that text to derive meaning. This allows for better data processing (i.e., step 1) in the speech analytics process.

Speech Sentiment Analysis

The actual "analysis" conducted in step 2 of the speech analytics process will vary greatly depending on the team's needs for performing the analysis. A business may only be interested in searching the speech transcript for specific keywords related to sales growth, for example, a relatively straightforward process. Or, they may be interested in performing a more advanced type of analysis, such as a tonality-based speech sentiment analysis.

Speech sentiment analysis is the process of identifying the positive, negative, and neutral sentiments of a recorded speech. Conducting this type of specific analysis requires the input and guidance of researchers educated in the field of speech sentiment. That is why many organizations have developed proprietary software, created with the input of skilled researchers, that can identify speakers' emotions in an audio file. Advancements in artificial intelligence have made this possible. For example, OSP Labs offers its clients a speech recognition software that utilizes a component of artificial intelligence called Natural Language Processing. This software analyzes speech nuances like tone to understand the speaker's emotions and sentiments better.

Speech analytics can be an essential tool for any business interested in learning more about their customers. Artificial intelligence has made it possible to automate the entire speech analytics process, from transcribing recorded calls to analyzing them at a nuanced level according to detailed criteria. If you're interested in learning how VoiceSignals' advanced People Intelligence Platform can deliver impactful speech analytics for your business, contact us today to schedule a demo.

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Written by
VoiceSignals Intelligence Team
VoiceSignals Technology and Industry Experts

A collection of thought-leaders, industry experts, and guest writers educated in all fields of voice-intelligence, Psych I/O, behavioral science, and conversation intelligence. Our team continually covers industry-related articles at the bleeding edge of technology and conversation intelligence.