To compare text-to-speech (TTS) engines, developers use a mix of subjective evaluations, objective metrics, and standardized datasets. Subjective assessments involve human listeners rating qualities like naturalness, intelligibility, and emotional expressiveness. For example, the Mean Opinion Score (MOS) is a widely used subjective benchmark where participants rate synthetic speech on a scale (e.g., 1–5). Objective metrics, on the other hand, quantify technical aspects algorithmically. Common measures include Mel-Cepstral Distortion (MCD) for assessing spectral accuracy, Word Error Rate (WER) to gauge how accurately an ASR system transcribes the output, and Root Mean Square Error (RMSE) for waveform similarity. These metrics help identify specific strengths or weaknesses, such as pronunciation errors or audio artifacts.
Standardized datasets and challenges provide consistent baselines for comparison. For instance, the Blizzard Challenge and Voice Conversion Challenge offer shared datasets (e.g., LJSpeech or VCTK corpora) and predefined evaluation protocols. These competitions often combine subjective and objective metrics, encouraging developers to optimize for both quality and technical performance. Another example is the CMU Arctic dataset, which includes recordings of multiple speakers and is used to benchmark speaker similarity via metrics like Speaker Encoder Cosine Similarity (comparing embeddings of synthesized and real speech). Such datasets ensure fair comparisons by controlling variables like recording conditions or text content.
Runtime performance metrics are critical for practical deployment. Developers often measure inference latency (time to generate audio), real-time factor (RTF: generation time divided by audio duration), and memory usage. For example, a TTS engine with an RTF of 0.5 can generate 1 second of audio in 0.5 seconds, making it suitable for real-time applications. Tools like ESPnet-TTS or TensorFlowTTS include built-in evaluation scripts for these metrics. Additionally, cross-platform compatibility (e.g., mobile vs. server) and scalability under load (requests per second) are often tested using frameworks like Apache Bench or Locust. By combining these benchmarks, developers can holistically assess TTS systems for both quality and operational efficiency.