You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify

Converting the speech to text first removes vocal characteristics such as pitch, tone, and accent, which can vary significantly with gender, age, and cultural backgrounds. By analyzing sentiment from the transcribed text, the model focuses solely on the semantic content, thereby reducing potential biases introduced by auditory features related to demographics.

While converting to text and using words is part of the process, extracting sentiments based on individual words rather than the full context of sentences might miss nuanced meanings and could still be prone to lexical biases if not carefully handled.

Extracting sentiment directly from voice recordings would likely amplify biases related to gender, age, and cultural differences, as vocal features often correlate with these attributes and could be misinterpreted as emotional cues.

Syntactical analysis focuses on grammar and sentence structure; while important in NLP, the primary step to specifically mitigate bias related to *voice characteristics* is the speech-to-text conversion itself, allowing subsequent semantic analysis of sentences for sentiment.