STT and TTS Development Services

Frequently Asked Questions

• Q:

  What speech AI solutions does Azumo build?

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  Azumo builds speech-to-text (STT) and text-to-speech (TTS) systems for real-time transcription, voice-enabled AI assistants, call center analytics, meeting summarization, voice command interfaces, and accessible content generation. We built a generative AI voice assistant for a gaming company that combines real-time speech recognition with LLM-powered dialogue and voice synthesis. Our speech stack includes OpenAI Whisper, Deepgram, and Azure Speech Services for transcription, and ElevenLabs, Azure Neural TTS, Amazon Polly, and open-source models like Coqui TTS for voice synthesis. We deploy on AWS, Azure, and Google Cloud with edge deployment options for low-latency applications. SOC 2 certified with nearshore engineering teams across Latin America.

• Q:

  What is the difference between speech-to-text and text-to-speech AI?

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  Speech-to-text (STT) converts spoken audio into written text. It handles real-time microphone input and recorded audio files, producing transcripts with speaker identification, timestamps, and punctuation. Text-to-speech (TTS) converts written text into natural-sounding spoken audio. Modern TTS uses neural networks to produce voices that are nearly indistinguishable from human speech, with control over tone, speed, emotion, and accent. Most production voice AI systems use both: STT captures user speech, NLP processes the meaning, the system generates a response, and TTS delivers it as spoken audio. Azumo builds end-to-end voice pipelines that combine STT, NLP/LLM reasoning, and TTS into seamless conversational experiences with sub-second latency for real-time applications.

• Q:

  What speech AI models and platforms does Azumo work with?

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  For speech-to-text: OpenAI Whisper (best accuracy across languages and accents), Deepgram (optimized for real-time streaming with speaker diarization), Azure Speech Services (enterprise-grade with custom model training), and Google Cloud Speech-to-Text. For text-to-speech: ElevenLabs (highest quality voice cloning and emotional range), Azure Neural TTS (broadest language coverage with custom voice creation), Amazon Polly (cost-effective for high-volume applications), and open-source models like Coqui TTS and Bark for on-premises deployment. We also work with Twilio and Vonage for telephony integration, WebRTC for browser-based voice, and NVIDIA Riva for GPU-accelerated on-device speech processing.

• Q:

  How does Azumo build voice-enabled AI assistants?

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  Voice assistants combine three layers: speech recognition (STT), reasoning (NLP/LLM), and voice output (TTS). Azumo builds each layer and optimizes the pipeline for end-to-end latency. The STT layer uses streaming transcription with voice activity detection to capture user speech in real time. The reasoning layer processes transcribed text through an LLM or NLP pipeline that understands intent, retrieves relevant context via RAG, and generates a response. The TTS layer converts the response to speech with appropriate tone and pacing. Total round-trip latency for a conversational voice assistant should stay under 1-2 seconds. We optimize this through model selection, caching, streaming inference, and edge deployment. Our gaming voice assistant project demonstrated real-time generative AI dialogue with natural-sounding voice output.

• Q:

  What industries benefit most from speech AI?

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  Speech AI delivers strong ROI in healthcare, call centers, media, education, gaming, and accessibility. Healthcare uses STT for clinical dictation and medical transcription, reducing documentation burden on physicians. Call centers automate quality assurance by transcribing and analyzing every call for sentiment, compliance, and coaching opportunities. Media companies use STT for automated captioning, subtitle generation, and content indexing across video libraries. Education platforms use TTS for accessible learning materials and language learning applications. Gaming companies build voice-driven characters and assistants. Accessibility: TTS makes digital content available to visually impaired users. Azumo has built speech AI for gaming and enterprise voice assistant applications.

• Q:

  How long does it take to build a speech AI system?

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  A proof-of-concept voice assistant or transcription system can be delivered in 1-2 weeks using pre-trained models. Production-ready speech AI with custom voice models, enterprise integrations, and real-time latency optimization typically takes 2-4 months. Key timeline factors include audio data quality and availability, custom model training requirements (domain-specific vocabulary, accent handling, custom voice creation), number of integrations, and latency targets. Custom voice creation using voice cloning typically adds 2-4 weeks. Real-time streaming optimization for sub-second latency may require additional tuning. Azumo accelerates delivery with pre-built speech pipelines and Valkyrie for model routing. Our nearshore teams work in US time zones.

• Q:

  How does Azumo handle accuracy challenges in speech-to-text?

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  STT accuracy depends on audio quality, background noise, speaker accents, domain vocabulary, and conversation overlap. Azumo addresses these through model selection (Whisper excels at noisy audio, Deepgram at real-time streaming), custom vocabulary injection for industry-specific terminology (medical terms, legal jargon, product names), speaker diarization to separate overlapping speakers, and noise reduction preprocessing. For domain-critical accuracy, we fine-tune models on your actual audio data. We evaluate STT systems using word error rate (WER) and character error rate (CER) on representative test sets from your environment. Multilingual transcription uses models trained on 100+ languages with automatic language detection. Post-processing pipelines add punctuation, format numbers, and correct common misrecognitions.

• Q:

  What security does Azumo implement for speech AI systems?

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  Azumo is SOC 2 certified and implements end-to-end encryption for audio data at rest and in transit. Voice data is biometric information under GDPR and BIPA (Illinois Biometric Information Privacy Act), requiring explicit consent and careful handling. We build consent management workflows, audio data retention policies, and deletion capabilities to meet regulatory requirements. PII detection runs on transcribed text to redact sensitive information before storage. For healthcare speech AI, we implement HIPAA-compliant audio processing with audit trails and access controls. We offer on-premises deployment for organizations that cannot send audio data to cloud APIs. Voice cloning projects include safeguards against misuse, including watermarking and usage restrictions.