can you share link to Twilio old fashion style code project voice to text - text to llm -text to voice
When having a voice conversation with an AI (like during a phone call), traditional voice AI solutions like VAPI or Bland handle each interaction through a three-step process:
Speech-to-text (STT): When you speak into your phone, your voice is first converted to text using a transcription model like DeepGram
Large Language Model: The transcribed text is then sent to an AI model like ChatGPT to generate a response
Text-to-speech (TTS): Finally, the AI’s text response is converted back into speech using a voice model like ElevenLabs
For example, if you call an AI agent and say “What’s the weather like today?”, your voice goes through all these conversions:
Voice → Text: “what’s the weather like today”
Text → AI Processing → Response Text: “The weather today is sunny with a high of 75°F”
Response Text → AI Voice
This multi-step process has several limitations:
Higher latency due to multiple model conversions
Loss of emotional context and tone (can’t tell if you’re excited or upset from text alone)
Cannot detect non-speech sounds (like background music or laughter)
Difficulty distinguishing homophones (like “live” performance vs “live” here)
Less natural conversation flow due to processing delays
can you share link to Twilio old fashion style code project voice to text - text to llm -text to voice
When having a voice conversation with an AI (like during a phone call), traditional voice AI solutions like VAPI or Bland handle each interaction through a three-step process:
Speech-to-text (STT): When you speak into your phone, your voice is first converted to text using a transcription model like DeepGram
Large Language Model: The transcribed text is then sent to an AI model like ChatGPT to generate a response
Text-to-speech (TTS): Finally, the AI’s text response is converted back into speech using a voice model like ElevenLabs
For example, if you call an AI agent and say “What’s the weather like today?”, your voice goes through all these conversions:
Voice → Text: “what’s the weather like today”
Text → AI Processing → Response Text: “The weather today is sunny with a high of 75°F”
Response Text → AI Voice
This multi-step process has several limitations:
Higher latency due to multiple model conversions
Loss of emotional context and tone (can’t tell if you’re excited or upset from text alone)
Cannot detect non-speech sounds (like background music or laughter)
Difficulty distinguishing homophones (like “live” performance vs “live” here)
Less natural conversation flow due to processing delays