From: aidotengineer
While text-based agents and chatbots are common, the “cool next frontier” in AI is voice AI, which is already transforming call centers globally [00:01:48]. Over one billion calls in call centers worldwide currently utilize voice APIs [00:01:55].
Applications of Voice AI
Real-time voice APIs are enabling AI agents to revolutionize call center operations [00:02:03]. A practical example of this is the Priceline Pennybot, a production-level travel agent that allows users to book an entire vacation hands-free, without needing to type any text [00:02:13].
Multimodal Agents
Beyond text-based interactions, the focus is expanding to multimodal agents [00:02:26]. These agents combine various modalities, such as voice and text, requiring a more complex approach to evaluation [00:02:29].
Evaluating Voice and Multimodal Applications
Voice applications are considered among the most complex types of applications ever built [00:11:54]. Evaluating them requires additional considerations beyond just text analysis [00:11:59].
Specific evaluation points for voice applications include:
- Audio Chunk Evaluation It’s crucial to evaluate not just the generated transcript but also the underlying audio chunks [00:12:07].
- User Sentiment Assessing the user’s sentiment from their voice [00:12:30].
- Speech-to-Text Accuracy Checking the accuracy of the speech-to-text transcription [00:12:34].
- Tone Consistency Ensuring the tone remains consistent throughout the conversation [00:12:36].
- Intent and Speech Quality Defining evaluations (evals) specifically for intent, speech quality, and speech-to-text accuracy related to the audio chunks [00:12:53].
While evaluating components of AI agents like the router, skill, and memory is standard, voice and multimodal agents necessitate these deeper, modality-specific evaluations to ensure real-world effectiveness [00:02:39].