With the increase of voice-controlled systems, speech based recognition applications are gaining more attention. Such applications need to adapt to hardware platforms to offer the required performance. Given the streaming nature of these applications, dataflow models are a common choice for model-based design and execution on parallel embedded platforms. However, most of today’s models are built on top of classical static dataflow with adaptivity extensions to express data parallelism. In this paper, we define and describe an approach for algorithmic adaptivity to express richer sets of variants and trade-offs. For this, we introduce multi-Alternative Process Network (mAPN), a high-level abstract representation where several process networks of the same application coexist. We describe an algorithm for automatic generation of all possible alternatives. The mAPN is enriched with meta-data serving to endow the alternatives with annotations in terms of a specific metric, helping to extract the most suitable alternative depending on the available computational resources and application/user constraints. We motivate the approach by the automatic subtitling application (ASA) as use case and run the experiments on an mAPN sample consisting of 12 randomly selected possible variants.