  Recent developments in natural language processing (NLP) have highlighted the
need for substantial amounts of data for models to capture textual information
accurately. This raises concerns regarding the computational resources and time
required for training such models. This paper introduces Semantics for data
SAliency in Model performance Estimation (SeSaME). It is an efficient data
sampling mechanism solely based on textual information without passing the data
through a compute-heavy model or other intensive pre-processing
transformations. The application of this approach is demonstrated in the use
case of low-resource automated speech recognition (ASR) models, which
excessively rely on text-to-speech (TTS) calls when using augmented data.
SeSaME learns to categorize new incoming data points into speech recognition
difficulty buckets by employing semantic similarity-based graph structures and
discrete ASR information from homophilous neighbourhoods through message
passing. The results indicate reliable projections of ASR performance, with a
93% accuracy increase when using the proposed method compared to random
predictions, bringing non-trivial information on the impact of textual
representations in speech models. Furthermore, a series of experiments show
both the benefits and challenges of using the ASR information on incoming data
to fine-tune the model. We report a 7% drop in validation loss compared to
random sampling, 7% WER drop with non-local aggregation when evaluating against
a highly difficult dataset, and 1.8% WER drop with local aggregation and high
semantic similarity between datasets.
